id stringlengths 10 10 | title stringlengths 8 162 | summary stringlengths 228 1.92k | source stringlengths 31 31 | authors stringlengths 7 6.97k | categories stringlengths 5 107 | comment stringlengths 4 398 ⌀ | journal_ref stringlengths 8 194 ⌀ | primary_category stringlengths 5 17 | published stringlengths 8 8 | updated stringlengths 8 8 | content stringlengths 3.91k 873k | references dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
2306.12001 | An Overview of Catastrophic AI Risks | Rapid advancements in artificial intelligence (AI) have sparked growing
concerns among experts, policymakers, and world leaders regarding the potential
for increasingly advanced AI systems to pose catastrophic risks. Although
numerous risks have been detailed separately, there is a pressing need for a
systematic discus... | http://arxiv.org/pdf/2306.12001 | Dan Hendrycks, Mantas Mazeika, Thomas Woodside | cs.CY, cs.AI, cs.LG | null | null | cs.CY | 20230621 | 20231009 | 3 2 0 2
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# An Overview of Catastrophic AI Risks
Dan Hendrycks Center for AI Safety Mantas Mazeika Center for AI Safety Thomas Woodside Center for AI Safety
# Abstract
Rapid advancements in artificial intelligence (AI) have sparked growing concerns among experts,... | {
"id": "1908.09203"
} |
2306.16527 | OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents | Large multimodal models trained on natural documents, which interleave images
and text, outperform models trained on image-text pairs on various multimodal
benchmarks. However, the datasets used to train these models have not been
released, and the collection process has not been fully specified. We introduce
the OBELI... | http://arxiv.org/pdf/2306.16527 | Hugo Laurençon, Lucile Saulnier, Léo Tronchon, Stas Bekman, Amanpreet Singh, Anton Lozhkov, Thomas Wang, Siddharth Karamcheti, Alexander M. Rush, Douwe Kiela, Matthieu Cord, Victor Sanh | cs.IR, cs.CV | null | null | cs.IR | 20230621 | 20230821 | 3 2 0 2
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ee) â
# OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
Hugo Laurençonâ,1,2 Lucile Saulnierâ,1 Léo Tronchonâ,1 Stas Bekmanâ,1 Amanpreet Singhâ,1 Anton Lozhkov1 Thomas Wang1 Siddharth Karamcheti1,3 Alexander... | {
"id": "2304.06939"
} |
2306.11698 | DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models | Generative Pre-trained Transformer (GPT) models have exhibited exciting
progress in their capabilities, capturing the interest of practitioners and the
public alike. Yet, while the literature on the trustworthiness of GPT models
remains limited, practitioners have proposed employing capable GPT models for
sensitive app... | http://arxiv.org/pdf/2306.11698 | Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li | cs.CL, cs.AI, cs.CR | NeurIPS 2023 Outstanding Paper (Datasets and Benchmarks Track) | null | cs.CL | 20230620 | 20240105 | 4 2 0 2
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# DECODINGTRUST: A Comprehensive Assessment of Trustworthiness in GPT Models
# Boxin Wang1â, Weixin Chen1â, Hengzhi Pei1â, Chulin Xie1â, Mintong Kang1â, Chenhui Zhang1â, Chejian Xu1, Zidi Xiong1, Ritik Dutta1, Rylan Schaeffer2, Sang T. Truong... | {
"id": "2302.13971"
} |
2306.11644 | Textbooks Are All You Need | We introduce phi-1, a new large language model for code, with significantly
smaller size than competing models: phi-1 is a Transformer-based model with
1.3B parameters, trained for 4 days on 8 A100s, using a selection of ``textbook
quality" data from the web (6B tokens) and synthetically generated textbooks
and exercis... | http://arxiv.org/pdf/2306.11644 | Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee, Yuanzhi Li | cs.CL, cs.AI, cs.LG | 26 pages; changed color scheme of plot. fixed minor typos and added
couple clarifications | null | cs.CL | 20230620 | 20231002 | 3 2 0 2
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# Textbooks Are All You Need
Suriya Gunasekar Allie Del Giorno Yi Zhang Sivakanth Gopi Jyoti Aneja Caio C´esar Teodoro Mendes Piero Kauffmann Mojan Javaheripi Gustavo de Rosa Xin Wang Olli Saarikivi S´ebastien Bubeck Adil Salim Ronen Eldan Shital Shah ... | {
"id": "2204.02311"
} |
2306.11507 | TrustGPT: A Benchmark for Trustworthy and Responsible Large Language Models | Large Language Models (LLMs) such as ChatGPT, have gained significant
attention due to their impressive natural language processing capabilities. It
is crucial to prioritize human-centered principles when utilizing these models.
Safeguarding the ethical and moral compliance of LLMs is of utmost importance.
However, ind... | http://arxiv.org/pdf/2306.11507 | Yue Huang, Qihui Zhang, Philip S. Y, Lichao Sun | cs.CL, cs.AI | We are currently expanding this work and welcome collaborators! | null | cs.CL | 20230620 | 20230620 | 3 2 0 2 n u J 0 2 ] L C . s c [
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# TRUSTGPT: A Benchmark for Trustworthy and Responsible Large Language Models
# Yue Huangâ Sichuan University huangyue1@stu.scu.edu.cn
# Qihui Zhang Sichuan University yolo_hui@stu.scu.edu.cn
# Philip S. Yu University of Illinois at Chicago psyu@uic.... | {
"id": "2305.12434"
} |
2306.11489 | Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling | Recently, ChatGPT, a representative large language model (LLM), has gained
considerable attention due to its powerful emergent abilities. Some researchers
suggest that LLMs could potentially replace structured knowledge bases like
knowledge graphs (KGs) and function as parameterized knowledge bases. However,
while LLMs... | http://arxiv.org/pdf/2306.11489 | Linyao Yang, Hongyang Chen, Zhao Li, Xiao Ding, Xindong Wu | cs.CL, cs.AI | null | null | cs.CL | 20230620 | 20240130 | 4 2 0 2 n a J 0 3 ] L C . s c [
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JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021
# Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling
Linyao Yang, Hongyang Chen, Senior Member, IEEE, Zhao Li, Xiao Ding, Xindong Wu, Fello... | {
"id": "2010.11967"
} |
2306.10512 | Efficiently Measuring the Cognitive Ability of LLMs: An Adaptive Testing Perspective | Large language models (LLMs), like ChatGPT, have shown some human-like
cognitive abilities. For comparing these abilities of different models, several
benchmarks (i.e. sets of standard test questions) from different fields (e.g.,
Literature, Biology and Psychology) are often adopted and the test results
under tradition... | http://arxiv.org/pdf/2306.10512 | Yan Zhuang, Qi Liu, Yuting Ning, Weizhe Huang, Rui Lv, Zhenya Huang, Guanhao Zhao, Zheng Zhang, Qingyang Mao, Shijin Wang, Enhong Chen | cs.CL | null | null | cs.CL | 20230618 | 20231028 | 3 2 0 2
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# Efficiently Measuring the Cognitive Ability of LLMs: An Adaptive Testing Perspective
Yan Zhuang1,2, Qi Liu1,2, Yuting Ning1,2, Weizhe Huang1,2, Rui Lv1,2, Zhenya Huang1,2, Guanhao Zhao1,2, Zheng Zhang1,2, Qingyang Mao1,2, Shijin Wang2, Enhong Chen1,2... | {
"id": "2305.02201"
} |
2306.09896 | Is Self-Repair a Silver Bullet for Code Generation? | Large language models have shown remarkable aptitude in code generation, but
still struggle on challenging tasks. Self-repair -- in which the model debugs
and fixes mistakes in its own code -- has recently become a popular way to
boost performance in these settings. However, only very limited studies on how
and when se... | http://arxiv.org/pdf/2306.09896 | Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, Armando Solar-Lezama | cs.CL, cs.AI, cs.PL, cs.SE | Added experiments for HumanEval (dataset) and Code Llama (model) | null | cs.CL | 20230616 | 20231017 | 3 2 0 2
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Preprint. Under review.
# IS SELF-REPAIR A SILVER BULLET FOR CODE GENERATION?
Theo X. Olausson1, Jianfeng Gao2, Armando Solar-Lezama1 â 1MIT CSAIL Jeevana Priya Inala2, Chenglong Wang2, 2Microsoft Research
# ABSTRACT
Large language models have shown ... | {
"id": "2211.16490"
} |
2306.09539 | Block-State Transformers | State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and au... | http://arxiv.org/pdf/2306.09539 | Mahan Fathi, Jonathan Pilault, Orhan Firat, Christopher Pal, Pierre-Luc Bacon, Ross Goroshin | cs.CL, cs.LG | NeurIPS'23 - Thirty-seventh Conference on Neural Information
Processing Systems | null | cs.CL | 20230615 | 20231030 | 3 2 0 2
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# Block-State Transformers
# Mahan Fathi123â Jonathan Pilault124â
Orhan Firat1 Christopher Pal24 Pierre-Luc Bacon23 Ross Goroshin1 1Google DeepMind 2Mila 3Université de Montréal 4Polytechnique Montréal
# Abstract
State space models (SSMs) have s... | {
"id": "1901.02860"
} |
2306.09442 | Explore, Establish, Exploit: Red Teaming Language Models from Scratch | Deploying large language models (LMs) can pose hazards from harmful outputs
such as toxic or false text. Prior work has introduced automated tools that
elicit harmful outputs to identify these risks. While this is a valuable step
toward securing models, these approaches rely on a pre-existing way to
efficiently classif... | http://arxiv.org/pdf/2306.09442 | Stephen Casper, Jason Lin, Joe Kwon, Gatlen Culp, Dylan Hadfield-Menell | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230615 | 20231011 | 3 2 0 2
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Preprint
EXPLORE, ESTABLISH, EXPLOIT: RED-TEAMING LANGUAGE MODELS FROM SCRATCH
Stephen Casper MIT CSAIL scasper@mit.edu
# Jason Lin Stanford University
# Joe Kwon MIT
Gatlen Culp MIT
# Dylan Hadfield-Menell MIT CSAIL
Warning: This paper contains AI-gen... | {
"id": "2205.12548"
} |
2306.09328 | WizMap: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings | Machine learning models often learn latent embedding representations that
capture the domain semantics of their training data. These embedding
representations are valuable for interpreting trained models, building new
models, and analyzing new datasets. However, interpreting and using embeddings
can be challenging due ... | http://arxiv.org/pdf/2306.09328 | Zijie J. Wang, Fred Hohman, Duen Horng Chau | cs.LG, cs.CL, cs.CV, cs.HC | 8 pages, 8 figures, Accepted to ACL 2023. For a demo video, see
https://youtu.be/8fJG87QVceQ. For a live demo, see
https://poloclub.github.io/wizmap. Code is available at
https://github.com/poloclub/wizmap | null | cs.LG | 20230615 | 20230615 | 3 2 0 2
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# WIZMAP: Scalable Interactive Visualization for Exploring Large Machine Learning Embeddings
# Zijie J. Wang Georgia Tech jayw@gatech.edu
# Fred Hohman Apple fredhohman@apple.com
# Duen Horng Chau Georgia Tech polo@gatech.edu
@ Search Panel 2 dialogue ... | {
"id": "1810.04805"
} |
2306.09299 | Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization | A hallmark property of explainable AI models is the ability to teach other
agents, communicating knowledge of how to perform a task. While Large Language
Models perform complex reasoning by generating explanations for their
predictions, it is unclear whether they also make good teachers for weaker
agents. To address th... | http://arxiv.org/pdf/2306.09299 | Swarnadeep Saha, Peter Hase, Mohit Bansal | cs.CL, cs.AI, cs.LG | NeurIPS 2023 (23 pages, 12 figures). Our code is available at
https://github.com/swarnaHub/ExplanationIntervention | null | cs.CL | 20230615 | 20231114 | 3 2 0 2
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# Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization
Swarnadeep Saha Peter Hase Mohit Bansal Department of Computer Science University of North Carolina at Chapel Hill {swarna, peter, mbansal}@cs.unc.edu
... | {
"id": "2302.13971"
} |
2306.09212 | CMMLU: Measuring massive multitask language understanding in Chinese | As the capabilities of large language models (LLMs) continue to advance,
evaluating their performance becomes increasingly crucial and challenging. This
paper aims to bridge this gap by introducing CMMLU, a comprehensive Chinese
benchmark that covers various subjects, including natural science, social
sciences, enginee... | http://arxiv.org/pdf/2306.09212 | Haonan Li, Yixuan Zhang, Fajri Koto, Yifei Yang, Hai Zhao, Yeyun Gong, Nan Duan, Timothy Baldwin | cs.CL | null | null | cs.CL | 20230615 | 20240117 | 4 2 0 2 n a J 7 1 ] L C . s c [
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Under review
# CMMLU: MEASURING MASSIVE MULTITASK LAN- GUAGE UNDERSTANDING IN CHINESE
Haonan Li1,2 Yixuan Zhang1 Yeyun Gong4 Nan Duan4 Timothy Baldwin1,5 1MBZUAI 4Microsoft Research Asia Fajri Koto1 Yifei Yang3 Hai Zhao3 2LibrAI 3Shanghai Jiao Tong Uni... | {
"id": "2302.13971"
} |
2306.08302 | Unifying Large Language Models and Knowledge Graphs: A Roadmap | Large language models (LLMs), such as ChatGPT and GPT4, are making new waves
in the field of natural language processing and artificial intelligence, due to
their emergent ability and generalizability. However, LLMs are black-box
models, which often fall short of capturing and accessing factual knowledge. In
contrast, ... | http://arxiv.org/pdf/2306.08302 | Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, Xindong Wu | cs.CL, cs.AI | A short version of this paper was accepted by IEEE Transactions on
Knowledge and Data Engineering (TKDE) | IEEE Transactions on Knowledge and Data Engineering (TKDE) 2024 | cs.CL | 20230614 | 20240125 | 4 2 0 2 n a J 5 2 ] L C . s c [
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JOURNAL OF LATEX CLASS FILES, VOL. ??, NO. ??, MONTH 20YY
# Unifying Large Language Models and Knowledge Graphs: A Roadmap
Shirui Pan, Senior Member, IEEE, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, Xindong Wu, Fellow, IEEE
AbstractâLarge languag... | {
"id": "2309.01538"
} |
2306.08568 | WizardCoder: Empowering Code Large Language Models with Evol-Instruct | Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated
exceptional performance in code-related tasks. However, most existing models
are solely pre-trained on extensive raw code data without instruction
fine-tuning. In this paper, we introduce WizardCoder, which empowers Code LLMs
with complex inst... | http://arxiv.org/pdf/2306.08568 | Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, Daxin Jiang | cs.CL, cs.AI | Large Language model, Code Generation, Code LLMs | null | cs.CL | 20230614 | 20230614 | 3 2 0 2 n u J 4 1 ] L C . s c [
1 v 8 6 5 8 0 . 6 0 3 2 : v i X r a
# WizardCoder: Empowering Code Large Language Models with Evol-Instruct
# Ziyang Luo2â Can Xu1â Pu Zhao1 Qingfeng Sun1 Xiubo Geng1 Wenxiang Hu1 Chongyang Tao1 Jing Ma2 Qingwei Lin1 Daxin Jiang1â
1Microsoft 2Hong Kong Baptist University {caxu,puzh... | {
"id": "2305.06161"
} |
2306.08640 | AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn | Recent research on Large Language Models (LLMs) has led to remarkable
advancements in general NLP AI assistants. Some studies have further explored
the use of LLMs for planning and invoking models or APIs to address more
general multi-modal user queries. Despite this progress, complex visual-based
tasks still remain ch... | http://arxiv.org/pdf/2306.08640 | Difei Gao, Lei Ji, Luowei Zhou, Kevin Qinghong Lin, Joya Chen, Zihan Fan, Mike Zheng Shou | cs.CV | Project page: https://showlab.github.io/assistgpt/ | null | cs.CV | 20230614 | 20230628 | 3 2 0 2
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# AssistGPT: A General Multi-modal Assistant that can Plan, Execute, Inspect, and Learn
# Difei Gao, Lei Ji, Luowei Zhou, Kevin Qinghong Lin, Joya Chen, Zihan Fan, Mike Zheng Shouâ
Show Lab, National University of Singapore, https://showlab.github.i... | {
"id": "2302.13971"
} |
2306.08651 | Toward Grounded Social Reasoning | Consider a robot tasked with tidying a desk with a meticulously constructed
Lego sports car. A human may recognize that it is not socially appropriate to
disassemble the sports car and put it away as part of the "tidying". How can a
robot reach that conclusion? Although large language models (LLMs) have
recently been u... | http://arxiv.org/pdf/2306.08651 | Minae Kwon, Hengyuan Hu, Vivek Myers, Siddharth Karamcheti, Anca Dragan, Dorsa Sadigh | cs.RO, cs.AI | null | null | cs.RO | 20230614 | 20230614 | 3 2 0 2
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# Toward Grounded Social Reasoning
Minae Kwon, Hengyuan Hu, Vivek Myersâ , Siddharth Karamcheti, Anca Draganâ , Dorsa Sadigh Stanford University, UC Berkeleyâ {mnkwon, hengyuan, skaramcheti, dorsa}@cs.stanford.edu, {vmyers, anca}@berkeley.eduâ
Ab... | {
"id": "1606.06565"
} |
2306.07906 | WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences | We present WebGLM, a web-enhanced question-answering system based on the
General Language Model (GLM). Its goal is to augment a pre-trained large
language model (LLM) with web search and retrieval capabilities while being
efficient for real-world deployments. To achieve this, we develop WebGLM with
strategies for the L... | http://arxiv.org/pdf/2306.07906 | Xiao Liu, Hanyu Lai, Hao Yu, Yifan Xu, Aohan Zeng, Zhengxiao Du, Peng Zhang, Yuxiao Dong, Jie Tang | cs.CL, cs.AI | Accepted to KDD 2023 | null | cs.CL | 20230613 | 20230613 | 3 2 0 2
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WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences Hanyu Laiâ laihy19@mails.tsinghua.edu.cn Tsinghua University Beijing, China
Yifan Xu xuyifan2001@gmail.com Tsinghua University Beijing, China
Aohan Zeng zah22@... | {
"id": "2208.03299"
} |
2306.07799 | ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer | Large-scale language models, like ChatGPT, have garnered significant media
attention and stunned the public with their remarkable capacity for generating
coherent text from short natural language prompts. In this paper, we aim to
conduct a systematic inspection of ChatGPT's performance in two controllable
generation ta... | http://arxiv.org/pdf/2306.07799 | Dongqi Pu, Vera Demberg | cs.CL, cs.AI, cs.LG | ACL-SRW 2023 | null | cs.CL | 20230613 | 20230613 | 3 2 0 2 n u J 3 1 ] L C . s c [
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# ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer
Dongqi Pu and Vera Demberg Department of Computer Science Department of Language Science and Technology Saarland Informatics Campus, Saarland Un... | {
"id": "2302.14229"
} |
2306.07209 | Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow | Various industries such as finance, meteorology, and energy generate vast
amounts of heterogeneous data every day. There is a natural demand for humans
to manage, process, and display data efficiently. However, it necessitates
labor-intensive efforts and a high level of expertise for these data-related
tasks. Consideri... | http://arxiv.org/pdf/2306.07209 | Wenqi Zhang, Yongliang Shen, Weiming Lu, Yueting Zhuang | cs.CL, cs.AI, cs.CE | null | null | cs.CL | 20230612 | 20230612 | 3 2 0 2 n u J 2 1 ] L C . s c [
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# Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow
Wenqi Zhang1, Yongliang Shen1, Weiming Lu1, Yueting Zhuang1 Zhejiang University1 {zhangwenqi, syl, luwm, yzhuang}@zju.edu.cn
# Abstract
Various industries such as finance, me... | {
"id": "2305.14318"
} |
2306.07174 | Augmenting Language Models with Long-Term Memory | Existing large language models (LLMs) can only afford fix-sized inputs due to
the input length limit, preventing them from utilizing rich long-context
information from past inputs. To address this, we propose a framework, Language
Models Augmented with Long-Term Memory (LongMem), which enables LLMs to
memorize long his... | http://arxiv.org/pdf/2306.07174 | Weizhi Wang, Li Dong, Hao Cheng, Xiaodong Liu, Xifeng Yan, Jianfeng Gao, Furu Wei | cs.CL | null | null | cs.CL | 20230612 | 20230612 | 3 2 0 2 n u J 2 1 ] L C . s c [
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# Augmenting Language Models with Long-Term Memory
Weizhi Wangâ , Li Dongâ¡, Hao Chengâ¡, Xiaodong Liuâ¡, Xifeng Yanâ , Jianfeng Gaoâ¡, Furu Weiâ¡ â University of California, Santa Barbara â¡Microsoft Research weizhiwang@ucsb.edu, {lidong1, ha... | {
"id": "2301.12866"
} |
2306.06924 | TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI | While several recent works have identified societal-scale and
extinction-level risks to humanity arising from artificial intelligence, few
have attempted an {\em exhaustive taxonomy} of such risks. Many exhaustive
taxonomies are possible, and some are useful -- particularly if they reveal new
risks or practical approac... | http://arxiv.org/pdf/2306.06924 | Andrew Critch, Stuart Russell | cs.AI, cs.CR, cs.CY, cs.LG, 68T01, I.2.0 | null | null | cs.AI | 20230612 | 20230614 | 3 2 0 2 n u J 4 1 ] I A . s c [
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# TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI
Andrew Critchâ critch@eecs.berkeley.edu
Stuart Russellâ russell@cs.berkeley.edu
June 16, 2023
# Abstract
While several recent works have identiï¬ed societal-scale and extinction-leve... | {
"id": "1903.08542"
} |
2306.06770 | Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis | Large language models (LLMs) offer significant promise as a knowledge source
for task learning. Prompt engineering has been shown to be effective for
eliciting knowledge from an LLM, but alone it is insufficient for acquiring
relevant, situationally grounded knowledge for an embodied agent learning novel
tasks. We desc... | http://arxiv.org/pdf/2306.06770 | James R. Kirk, Robert E. Wray, Peter Lindes | cs.AI, cs.HC, cs.RO, I.2.6; I.2.7 | 7 pages, 8 figures, 3 tables, bibliography, appendix (34 pages
total). Text revised and results extended with additional tasks | null | cs.AI | 20230611 | 20230822 | 3 2 0 2
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# Improving Knowledge Extraction from LLMs for Task Learning through Agent Analysis
James R. Kirk, Robert E. Wray, Peter Lindes, John E. Laird Center for Integrated Cognition at IQMRI Ann Arbor, MI 48105 USA {james.kirk,robert.wray,peter.lindes,john.la... | {
"id": "2302.06706"
} |
2306.06624 | RestGPT: Connecting Large Language Models with Real-World RESTful APIs | Tool-augmented large language models (LLMs) have achieved remarkable progress
in tackling a broad range of tasks. However, existing methods are mainly
restricted to specifically designed tools and fail to fulfill complex
instructions, having great limitations when confronted with real-world
scenarios. In this paper, we... | http://arxiv.org/pdf/2306.06624 | Yifan Song, Weimin Xiong, Dawei Zhu, Wenhao Wu, Han Qian, Mingbo Song, Hailiang Huang, Cheng Li, Ke Wang, Rong Yao, Ye Tian, Sujian Li | cs.CL | Add RestBench to evaluate RestGPT | null | cs.CL | 20230611 | 20230827 | 3 2 0 2
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# RestGPT: Connecting Large Language Models with Real-World RESTful APIs
Yifan Song1, Weimin Xiong1, Dawei Zhu1, Wenhao Wu1, Han Qian2, Mingbo Song2 Hailiang Huang2, Cheng Li3, Ke Wang3, Rong Yao3, Ye Tian3, Sujian Li1â 1School of Computer Science, P... | {
"id": "2302.04761"
} |
2306.07932 | Human-in-the-Loop through Chain-of-Thought | While the emergence of powerful language models along with Chain-of-thought
prompting has made automation more and more omnipresent, it sometimes
demonstrates its weakness in long-term or multi-step logical reasoning. For
example, users don't always get desirable answers for complex mathematical
problems without human ... | http://arxiv.org/pdf/2306.07932 | Zefan Cai, Baobao Chang, Wenjuan Han | cs.CL, cs.AI | null | null | cs.CL | 20230610 | 20230623 | 3 2 0 2 n u J 3 2 ] L C . s c [
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# Human-in-the-Loop through Chain-of-Thought
Zefan Cai1,2, Baobao Chang1Ë, Wenjuan Han3Ë, 1National Key Laboratory for Multimedia Information Processing, Peking University 2School of Software and Microelectronics, Peking University, China 3Beijing Ji... | {
"id": "1904.09751"
} |
2306.06531 | AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers | For effective human-robot interaction, robots need to understand, plan, and
execute complex, long-horizon tasks described by natural language. Recent
advances in large language models (LLMs) have shown promise for translating
natural language into robot action sequences for complex tasks. However,
existing approaches e... | http://arxiv.org/pdf/2306.06531 | Yongchao Chen, Jacob Arkin, Charles Dawson, Yang Zhang, Nicholas Roy, Chuchu Fan | cs.RO, cs.CL, cs.HC | 8 pages, 4 figures | null | cs.RO | 20230610 | 20230927 | 3 2 0 2
p e S 7 2 ] O R . s c [ 2 v 1 3 5 6 0 . 6 0 3 2 : v i X r a
# AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers
Yongchao Chen1,2, Jacob Arkin1, Charles Dawson1, Yang Zhang3, Nicholas Roy1, and Chuchu Fan1
Abstractâ For effective human-robot interaction, robots need to und... | {
"id": "1706.06927"
} |
2306.06331 | Investigating the Effectiveness of ChatGPT in Mathematical Reasoning and Problem Solving: Evidence from the Vietnamese National High School Graduation Examination | This study offers a complete analysis of ChatGPT's mathematics abilities in
responding to multiple-choice questions for the Vietnamese National High School
Graduation Examination (VNHSGE) on a range of subjects and difficulty levels.
The dataset included 250 questions divided into four levels: knowledge (K),
comprehens... | http://arxiv.org/pdf/2306.06331 | Xuan-Quy Dao, Ngoc-Bich Le | cs.CL, cs.LG | 17 pages, 14 images | null | cs.CL | 20230610 | 20231031 | 3 2 0 2
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Investigating the Effectiveness of ChatGPT in Mathematical Reasoning and Problem Solving: Evidence from the Vietnamese National High School Graduation Examination
# Xuan-Quy Dao
School of Engineering Eastern International University Binh Duong, Vietnam... | {
"id": "2303.08774"
} |
2306.05949 | Evaluating the Social Impact of Generative AI Systems in Systems and Society | Generative AI systems across modalities, ranging from text, image, audio, and
video, have broad social impacts, but there exists no official standard for
means of evaluating those impacts and which impacts should be evaluated. We
move toward a standard approach in evaluating a generative AI system for any
modality, in ... | http://arxiv.org/pdf/2306.05949 | Irene Solaiman, Zeerak Talat, William Agnew, Lama Ahmad, Dylan Baker, Su Lin Blodgett, Hal Daumé III, Jesse Dodge, Ellie Evans, Sara Hooker, Yacine Jernite, Alexandra Sasha Luccioni, Alberto Lusoli, Margaret Mitchell, Jessica Newman, Marie-Therese Png, Andrew Strait, Apostol Vassilev | cs.CY, cs.AI | null | null | cs.CY | 20230609 | 20230612 | 3 2 0 2
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# Evaluating the Social Impact of Generative AI Systems in Systems and Society
Irene Solaimanâ Hugging Face Zeerak Talatâ Independent Researcher William Agnew University of Washington Lama Ahmad OpenAI Dylan Baker DAIR Su Lin Blodgett Microsoft Res... | {
"id": "2007.04068"
} |
2306.05720 | Beyond Surface Statistics: Scene Representations in a Latent Diffusion Model | Latent diffusion models (LDMs) exhibit an impressive ability to produce
realistic images, yet the inner workings of these models remain mysterious.
Even when trained purely on images without explicit depth information, they
typically output coherent pictures of 3D scenes. In this work, we investigate a
basic interpreta... | http://arxiv.org/pdf/2306.05720 | Yida Chen, Fernanda Viégas, Martin Wattenberg | cs.CV, cs.AI, cs.LG | A short version of this paper is accepted in the NeurIPS 2023
Workshop on Diffusion Models: https://nips.cc/virtual/2023/74894 | null | cs.CV | 20230609 | 20231104 | 3 2 0 2
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# Beyond Surface Statistics: Scene Representations in a Latent Diffusion Model
Yida Chen Harvard University Cambridge, MA 02138 yidachen@g.harvard.edu
Fernanda Viégas Harvard University Cambridge, MA 02138 fernanda@g.harvard.edu
Martin Wattenberg Harvar... | {
"id": "2209.14988"
} |
2306.05783 | Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation | New Natural Langauge Process~(NLP) benchmarks are urgently needed to align
with the rapid development of large language models (LLMs). We present Xiezhi,
the most comprehensive evaluation suite designed to assess holistic domain
knowledge. Xiezhi comprises multiple-choice questions across 516 diverse
disciplines rangin... | http://arxiv.org/pdf/2306.05783 | Zhouhong Gu, Xiaoxuan Zhu, Haoning Ye, Lin Zhang, Jianchen Wang, Sihang Jiang, Zhuozhi Xiong, Zihan Li, Qianyu He, Rui Xu, Wenhao Huang, Zili Wang, Shusen Wang, Weiguo Zheng, Hongwei Feng, Yanghua Xiao | cs.CL | Under review of NeurIPS 2023 | null | cs.CL | 20230609 | 20230615 | 3 2 0 2
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# Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation
Zhouhong Guâ ,1 Xiaoxuan Zhuâ,1,2 Haoning Ye1 Lin Zhang1 Jianchen Wang1 Rui Xu1 Sihang Jiang1 Zhuozhi Xiong1 Zihan Li1 Wenhao Huang1 Zili Wang3 Shusen Wang3 Yanghua Xiao... | {
"id": "2301.13126"
} |
2306.05817 | How Can Recommender Systems Benefit from Large Language Models: A Survey | Recommender systems (RS) play important roles to match users' information
needs for Internet applications. In natural language processing (NLP) domains,
large language model (LLM) has shown astonishing emergent abilities (e.g.,
instruction following, reasoning), thus giving rise to the promising research
direction of a... | http://arxiv.org/pdf/2306.05817 | Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, Weinan Zhang | cs.IR, cs.AI | 15 pages; 3 figures; summarization table in appendix | null | cs.IR | 20230609 | 20230628 | 3 2 0 2
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# How Can Recommender Systems Beneï¬t from Large Language Models: A Survey
Jianghao Lin1â , Xinyi Dai2â , Yunjia Xi1 , Weiwen Liu2 , Bo Chen2 , Xiangyang Li2 , Chenxu Zhu2 , Huifeng Guo2 , Yong Yu1 , Ruiming Tang2â , Weinan Zhang1â 1Shanghai J... | {
"id": "2302.13971"
} |
2306.05685 | Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena | Evaluating large language model (LLM) based chat assistants is challenging
due to their broad capabilities and the inadequacy of existing benchmarks in
measuring human preferences. To address this, we explore using strong LLMs as
judges to evaluate these models on more open-ended questions. We examine the
usage and lim... | http://arxiv.org/pdf/2306.05685 | Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, Ion Stoica | cs.CL, cs.AI | NeurIPS 2023 Datasets and Benchmarks Track | null | cs.CL | 20230609 | 20231224 | 3 2 0 2 c e D 4 2
] L C . s c [
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# Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
Lianmin Zheng1â Wei-Lin Chiang1â Ying Sheng4â Siyuan Zhuang1 Zhanghao Wu1 Yonghao Zhuang3 Zi Lin2 Zhuohan Li1 Dacheng Li13 Eric P. Xing35 Hao Zhang12 Joseph E. Gonzalez1 Ion Stoica1 2 UC Sa... | {
"id": "2302.13971"
} |
2306.06264 | Measuring and Modifying Factual Knowledge in Large Language Models | Large Language Models (LLMs) store an extensive amount of factual knowledge
obtained from vast collections of text. To effectively utilize these models for
downstream tasks, it is crucial to have reliable methods for measuring their
knowledge. However, existing approaches for knowledge measurement have certain
limitati... | http://arxiv.org/pdf/2306.06264 | Pouya Pezeshkpour | cs.CL, cs.LG | null | null | cs.CL | 20230609 | 20230609 | 3 2 0 2
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# Measuring and Modifying Factual Knowledge in Large Language Models
# Pouya Pezeshkpour Megagon Labs pouya@megagon.ai
# Abstract
and quantify the extent of LLMsâ knowledge about various facts.
Large Language Models (LLMs) store an ex- tensive amount o... | {
"id": "2302.13971"
} |
2306.06283 | 14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon | Large-language models (LLMs) such as GPT-4 caught the interest of many
scientists. Recent studies suggested that these models could be useful in
chemistry and materials science. To explore these possibilities, we organized a
hackathon.
This article chronicles the projects built as part of this hackathon.
Participants... | http://arxiv.org/pdf/2306.06283 | Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali, Shruti Badhwar, Joshua D. Bocarsly, Andres M Bran, Stefan Bringuier, L. Catherine Brinson, Kamal Choudhary, Defne Circi, Sam Cox, Wibe A. de Jong, Matthew L. Evans, Nicolas Gastellu, Jerome Genzling, María Victoria Gil, Ankur K. Gupta, Zhi Hong, Alishba Imran, Sa... | cond-mat.mtrl-sci, cs.LG, physics.chem-ph | null | null | cond-mat.mtrl-sci | 20230609 | 20230714 | 3 2 0 2
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# 14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reï¬ection on a Large Language Model
# Hackathon
Kevin Maik Jablonka ,1, â Qianxiang Ai ,2, â Alexander Al-Feghali ,3, â
Shruti Badhwar ,4, â Jo... | {
"id": "2209.08203"
} |
2306.07929 | Large Language Models Are Semi-Parametric Reinforcement Learning Agents | Inspired by the insights in cognitive science with respect to human memory
and reasoning mechanism, a novel evolvable LLM-based (Large Language Model)
agent framework is proposed as REMEMBERER. By equipping the LLM with a
long-term experience memory, REMEMBERER is capable of exploiting the
experiences from the past epi... | http://arxiv.org/pdf/2306.07929 | Danyang Zhang, Lu Chen, Situo Zhang, Hongshen Xu, Zihan Zhao, Kai Yu | cs.CL, cs.AI | null | null | cs.CL | 20230609 | 20231030 | 3 2 0 2
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# Large Language Models Are Semi-Parametric Reinforcement Learning Agents
# Danyang Zhang1
# Lu Chen1,2 â
# Situo Zhang1
Kai Yu1,2 1X-LANCE Lab, Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence, SJTU AI Institute ... | {
"id": "2201.06009"
} |
2306.05715 | Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests | Background and Context: Over the past year, large language models (LLMs) have
taken the world by storm. In computing education, like in other walks of life,
many opportunities and threats have emerged as a consequence.
Objectives: In this article, we explore such opportunities and threats in a
specific area: respondi... | http://arxiv.org/pdf/2306.05715 | Arto Hellas, Juho Leinonen, Sami Sarsa, Charles Koutcheme, Lilja Kujanpää, Juha Sorva | cs.CY, cs.AI, cs.CL, cs.HC, cs.SE | 13 pages, 1 figure. To be published in Proceedings of the 2023 ACM
Conference on International Computing Education Research V.1 (ICER '23 V1) | null | cs.CY | 20230609 | 20230609 | 3 2 0 2 n u J 9 ] Y C . s c [
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# Exploring the Responses of Large Language Models to Beginner Programmersâ Help Requests
Arto Hellas Aalto University Finland arto.hellas@aalto.ï¬
Juho Leinonen The University of Auckland New Zealand juho.leinonen@auckland.ac.nz
Sami Sarsa Aalto Univ... | {
"id": "2004.09456"
} |
2306.05087 | PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization | Instruction tuning large language models (LLMs) remains a challenging task,
owing to the complexity of hyperparameter selection and the difficulty involved
in evaluating the tuned models. To determine the optimal hyperparameters, an
automatic, robust, and reliable evaluation benchmark is essential. However,
establishin... | http://arxiv.org/pdf/2306.05087 | Yidong Wang, Zhuohao Yu, Zhengran Zeng, Linyi Yang, Cunxiang Wang, Hao Chen, Chaoya Jiang, Rui Xie, Jindong Wang, Xing Xie, Wei Ye, Shikun Zhang, Yue Zhang | cs.CL, cs.AI | null | null | cs.CL | 20230608 | 20230608 | 3 2 0 2
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# PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization
Yidong Wang1,2â, Zhuohao Yu1â, Zhengran Zeng1, Linyi Yang2, Cunxiang Wang2, Hao Chen3, Chaoya Jiang1, Rui Xie1, Jindong Wang3, Xing Xie3, Wei Ye1â , Shikun Zhang1â... | {
"id": "2302.13971"
} |
2306.05171 | Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures | Traditional robot task planning methods face challenges when dealing with
highly unstructured environments and complex tasks. We propose a task planning
method that combines human expertise with an LLM and have designed an LLM
prompt template, Think_Net_Prompt, with stronger expressive power to represent
structured pro... | http://arxiv.org/pdf/2306.05171 | Yue Zhen, Sheng Bi, Lu Xing-tong, Pan Wei-qin, Shi Hai-peng, Chen Zi-rui, Fang Yi-shu | cs.RO, cs.AI | null | null | cs.RO | 20230608 | 20230608 | Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
Yue Zhen1) Sheng Bi2) Lu Xing-tong3) Pan Wei-qin4) Shi Hai-peng5) Chen Zi-rui6) Fang Yi-shu7)
1)~6) South China University of Technology, School of Computer Science and Engineering, Guang Zhou 510006 7)University of ... | {
"id": "2302.12927"
} |
2306.05152 | Towards Autonomous Testing Agents via Conversational Large Language Models | Software testing is an important part of the development cycle, yet it
requires specialized expertise and substantial developer effort to adequately
test software. Recent discoveries of the capabilities of large language models
(LLMs) suggest that they can be used as automated testing assistants, and thus
provide helpf... | http://arxiv.org/pdf/2306.05152 | Robert Feldt, Sungmin Kang, Juyeon Yoon, Shin Yoo | cs.SE | null | null | cs.SE | 20230608 | 20230905 | # ee
3 2 0 2 p e S 5 ] E S . s c [
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# Towards Autonomous Testing Agents via Conversational Large Language Models
# Robert Feldt Chalmers University of Technology robert.feldt@chalmers.se
# Sungmin Kang KAIST sungmin.kang@kaist.ac.kr
# Juyeon Yoon KAIST juyeon.yoon@kaist.ac.kr
# Shin Y... | {
"id": "2305.10601"
} |
2306.05212 | RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit | Although Large Language Models (LLMs) have demonstrated extraordinary
capabilities in many domains, they still have a tendency to hallucinate and
generate fictitious responses to user requests. This problem can be alleviated
by augmenting LLMs with information retrieval (IR) systems (also known as
retrieval-augmented L... | http://arxiv.org/pdf/2306.05212 | Jiongnan Liu, Jiajie Jin, Zihan Wang, Jiehan Cheng, Zhicheng Dou, Ji-Rong Wen | cs.IR | Technical Report for RETA-LLM | null | cs.IR | 20230608 | 20230608 | 3 2 0 2
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# RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
Jiongnan Liu1, Jiajie Jin2, Zihan Wang1, Jiehan Cheng1, Zhicheng Dou1â, and Ji-Rong Wen1 1Gaoling School of Artificial Intelligence, Renmin University of China 2University of Science and Te... | {
"id": "2210.02414"
} |
2306.05301 | ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases | Enabling large language models to utilize real-world tools effectively is
crucial for achieving embodied intelligence. Existing approaches to tool
learning have either primarily relied on extremely large language models, such
as GPT-4, to attain generalized tool-use abilities in a zero-shot manner, or
utilized supervis... | http://arxiv.org/pdf/2306.05301 | Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, Le Sun | cs.CL | null | null | cs.CL | 20230608 | 20230907 | 3 2 0 2
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ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases Qiaoyu Tang1,3, Ziliang Deng1,3, Hongyu Lin1*, Xianpei Han1,2*, Qiao Liang1,3, Boxi Cao1,3, Le Sun1,2 1Chinese Information Processing Laboratory 2State Key Laboratory of ... | {
"id": "2305.16504"
} |
2306.05424 | Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models | Conversation agents fueled by Large Language Models (LLMs) are providing a
new way to interact with visual data. While there have been initial attempts
for image-based conversation models, this work addresses the underexplored
field of video-based conversation by introducing Video-ChatGPT. It is a
multimodal model that... | http://arxiv.org/pdf/2306.05424 | Muhammad Maaz, Hanoona Rasheed, Salman Khan, Fahad Shahbaz Khan | cs.CV | null | null | cs.CV | 20230608 | 20230608 | 3 2 0 2
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# Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
Muhammad Maaz*, Hanoona Rasheed*, Salman Khan, Fahad Shahbaz Khan muhammad.maaz@mbzuai.ac.ae, hanoona.bangalath@mbzuai.ac.ae Mohamed bin Zayed University of AI
# A... | {
"id": "2103.07461"
} |
2306.04610 | The Two Word Test: A Semantic Benchmark for Large Language Models | Large Language Models (LLMs) have shown remarkable abilities recently,
including passing advanced professional exams and demanding benchmark tests.
This performance has led many to suggest that they are close to achieving
humanlike or 'true' understanding of language, and even Artificial General
Intelligence (AGI). Her... | http://arxiv.org/pdf/2306.04610 | Nicholas Riccardi, Rutvik H. Desai | cs.CL, cs.AI | 12 pages, 5 figures, 3 tables, submitted to NeurIPS 2023 Datasets and
Benchmarks Track | null | cs.CL | 20230607 | 20230607 | # The Two Word Test: A Semantic Benchmark for Large Language Models
Nicholas Riccardi and Rutvik H. Desai
University of South Carolina
Department of Psychology
# Abstract
Large Language Models (LLMs) have shown remarkable abilities recently, including passing advanced professional exams and demanding benchmark tests. T... | {
"id": "2302.06476"
} |
2306.04181 | Benchmarking Foundation Models with Language-Model-as-an-Examiner | Numerous benchmarks have been established to assess the performance of
foundation models on open-ended question answering, which serves as a
comprehensive test of a model's ability to understand and generate language in
a manner similar to humans. Most of these works focus on proposing new
datasets, however, we see two... | http://arxiv.org/pdf/2306.04181 | Yushi Bai, Jiahao Ying, Yixin Cao, Xin Lv, Yuze He, Xiaozhi Wang, Jifan Yu, Kaisheng Zeng, Yijia Xiao, Haozhe Lyu, Jiayin Zhang, Juanzi Li, Lei Hou | cs.CL, cs.LG | NeurIPS 2023 Datasets and Benchmarks | null | cs.CL | 20230607 | 20231104 | 3 2 0 2
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# Benchmarking Foundation Models with Language-Model-as-an-Examiner
# Yushi Bai!*, Jiahao Ying?*, Yixin Caoâ, Xin Lv', Yuze Heâ, Xiaozhi Wangâ, Jifan Yu', Kaisheng Zeng', Yijia Xiao*, Haozhe Lyuâ, Jiayin Zhang!, Juanzi Li', Lei Hou!â¢
1Tsinghua ... | {
"id": "2302.13971"
} |
2306.04504 | Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with Fine-Tuned Generative Transformers | ChatGPT is a large language model developed by OpenAI. Despite its impressive
performance across various tasks, no prior work has investigated its capability
in the biomedical domain yet. To this end, this paper aims to evaluate the
performance of ChatGPT on various benchmark biomedical tasks, such as relation
extracti... | http://arxiv.org/pdf/2306.04504 | Israt Jahan, Md Tahmid Rahman Laskar, Chun Peng, Jimmy Huang | cs.CL, cs.LG | Accepted by BioNLP@ACL 2023 | null | cs.CL | 20230607 | 20230824 | 3 2 0 2
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Evaluation of ChatGPT on Biomedical Tasks: A Zero-Shot Comparison with Fine-Tuned Generative Transformers Israt Jahanâ , $, Md Tahmid Rahman Laskarâ¡, $, §, Chun Pengâ , Jimmy Xiangji Huangâ¡, $ â Department of Biology, York University â¡School ... | {
"id": "2302.04023"
} |
2306.04563 | ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models | Humor is a central aspect of human communication that has not been solved for
artificial agents so far. Large language models (LLMs) are increasingly able to
capture implicit and contextual information. Especially, OpenAI's ChatGPT
recently gained immense public attention. The GPT3-based model almost seems to
communica... | http://arxiv.org/pdf/2306.04563 | Sophie Jentzsch, Kristian Kersting | cs.AI, cs.CL, cs.HC, cs.LG | null | null | cs.AI | 20230607 | 20230607 | 3 2 0 2
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# ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models
Sophie Jentzsch1 and Kristian Kersting2,3,4 1Institute for Software Technology, German Aerospace Center (DLR), Cologne, Germany 2Computer Science Department, Technica... | {
"id": "2302.13971"
} |
2306.04528 | PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts | The increasing reliance on Large Language Models (LLMs) across academia and
industry necessitates a comprehensive understanding of their robustness to
prompts. In response to this vital need, we introduce PromptBench, a robustness
benchmark designed to measure LLMs' resilience to adversarial prompts. This
study uses a ... | http://arxiv.org/pdf/2306.04528 | Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Yue Zhang, Neil Zhenqiang Gong, Xing Xie | cs.CL, cs.CR, cs.LG | Technical report; code is at:
https://github.com/microsoft/promptbench | null | cs.CL | 20230607 | 20231018 | 3 2 0 2
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# PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
Kaijie Zhu1,2â, Jindong Wang1â , Jiaheng Zhou2, Zeek Wang1, Hao Chen3, Yidong Wang4, Linyi Yang5, Wei Ye4, Yue Zhang5, Neil Zhenqiang Gong6, Xing Xie1
1... | {
"id": "1705.00440"
} |
2306.04618 | Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations | This paper reexamines the research on out-of-distribution (OOD) robustness in
the field of NLP. We find that the distribution shift settings in previous
studies commonly lack adequate challenges, hindering the accurate evaluation of
OOD robustness. To address these issues, we propose a benchmark construction
protocol t... | http://arxiv.org/pdf/2306.04618 | Lifan Yuan, Yangyi Chen, Ganqu Cui, Hongcheng Gao, Fangyuan Zou, Xingyi Cheng, Heng Ji, Zhiyuan Liu, Maosong Sun | cs.CL, cs.CR, cs.LG | Accepted to NeurIPS 2023 Dataset and Benchmark Track. Code is
available at \url{https://github.com/lifan-yuan/OOD_NLP} | null | cs.CL | 20230607 | 20231026 | 3 2 0 2
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# Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations
Lifan Yuan1, Yangyi Chen2, Ganqu Cui1, Hongcheng Gao3, Fangyuan Zou4, Xingyi Cheng4, Heng Ji2, Zhiyuan Liu1â, Maosong Sun1â 1 NLP Group, DCST, IAI, BNRIST... | {
"id": "2006.00632"
} |
2306.04751 | How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources | In this work we explore recent advances in instruction-tuning language models
on a range of open instruction-following datasets. Despite recent claims that
open models can be on par with state-of-the-art proprietary models, these
claims are often accompanied by limited evaluation, making it difficult to
compare models ... | http://arxiv.org/pdf/2306.04751 | Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Raghavi Chandu, David Wadden, Kelsey MacMillan, Noah A. Smith, Iz Beltagy, Hannaneh Hajishirzi | cs.CL | 18 pages, 6 figure, 10 tables. NeurIPS 2023 Datasets and Benchmarks
Track Camera Ready | null | cs.CL | 20230607 | 20231030 | 3 2 0 2
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# How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
# Yizhong Wangâ â£â Hamish Ivisonâ ⣠Pradeep Dasigi⣠Jack Hessel⣠Tushar Khot⣠Khyathi Raghavi Chandu⣠David Wadden⣠Kelsey MacMillan⣠Noah A. Smi... | {
"id": "2302.13971"
} |
2306.04757 | INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models | Instruction-tuned large language models have revolutionized natural language
processing and have shown great potential in applications such as
conversational agents. These models, such as GPT-4, can not only master
language but also solve complex tasks in areas like mathematics, coding,
medicine, and law. Despite their... | http://arxiv.org/pdf/2306.04757 | Yew Ken Chia, Pengfei Hong, Lidong Bing, Soujanya Poria | cs.CL, cs.AI | Github: https://github.com/declare-lab/instruct-eval Leaderboard:
https://declare-lab.github.io/instruct-eval/ | null | cs.CL | 20230607 | 20230615 | 3 2 0 2
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# INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models
Yew Ken Chiaâ¡â , Pengfei Hongâ¡, Lidong Bingâ , Soujanya Poriaâ¡ â¡ DeCLaRe Lab, Singapore University of Technology and Design, Singapore â DAMO Academy, Al... | {
"id": "2301.13688"
} |
2306.04031 | Certified Deductive Reasoning with Language Models | Language models often achieve higher accuracy when reasoning step-by-step in
complex tasks. However, even when arriving at a correct final answer, their
rationales are often logically unsound or inconsistent. This is a major issue
when reliable reasoning traces are needed, such when fine-tuning on
model-generated reaso... | http://arxiv.org/pdf/2306.04031 | Gabriel Poesia, Kanishk Gandhi, Eric Zelikman, Noah D. Goodman | cs.AI | null | null | cs.AI | 20230606 | 20231108 | 3 2 0 2 v o N 8 ] I A . s c [
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# CERTIFIED DEDUCTIVE REASONING WITH LANGUAGE MODELS
# Gabriel Poesia, Kanishk Gandhiâ, Eric Zelikmanâ, Noah D. Goodman Stanford University {poesia,kanishkg,ezelikman,ngoodman}@stanford.edu
# ABSTRACT
Language models often achieve higher accuracy whe... | {
"id": "2302.13971"
} |
2306.03917 | Turning large language models into cognitive models | Large language models are powerful systems that excel at many tasks, ranging
from translation to mathematical reasoning. Yet, at the same time, these models
often show unhuman-like characteristics. In the present paper, we address this
gap and ask whether large language models can be turned into cognitive models.
We fi... | http://arxiv.org/pdf/2306.03917 | Marcel Binz, Eric Schulz | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230606 | 20230606 | 3 2 0 2 n u J 6 ] L C . s c [
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# Turning large language models into cognitive models
Marcel Binz MPRG Computational Principles of Intelligence Max Planck Institute for Biological Cybernetics, Tübingen, Germany marcel.binz@tue.mpg.de
Eric Schulz MPRG Computational Principles of Intell... | {
"id": "2302.13971"
} |
2306.03901 | ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory | Large language models (LLMs) with memory are computationally universal.
However, mainstream LLMs are not taking full advantage of memory, and the
designs are heavily influenced by biological brains. Due to their approximate
nature and proneness to the accumulation of errors, conventional neural memory
mechanisms cannot... | http://arxiv.org/pdf/2306.03901 | Chenxu Hu, Jie Fu, Chenzhuang Du, Simian Luo, Junbo Zhao, Hang Zhao | cs.AI, cs.CL, cs.DB, cs.LG | null | null | cs.AI | 20230606 | 20230607 | 3 2 0 2 n u J 7 ] I A . s c [
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# CHATDB: AUGMENTING LLMS WITH DATABASES AS THEIR SYMBOLIC MEMORY
Chenxu Hu1â Jie Fu2â â Chenzhuang Du1 Simian Luo1 1Tsinghua University 2Beijing Academy of Artificial Intelligence Junbo Zhao3 Hang Zhao1â 3Zhejiang University fujie@baai.ac.cn han... | {
"id": "2302.13971"
} |
2306.03872 | Deductive Verification of Chain-of-Thought Reasoning | Large Language Models (LLMs) significantly benefit from Chain-of-Thought
(CoT) prompting in performing various reasoning tasks. While CoT allows models
to produce more comprehensive reasoning processes, its emphasis on intermediate
reasoning steps can inadvertently introduce hallucinations and accumulated
errors, there... | http://arxiv.org/pdf/2306.03872 | Zhan Ling, Yunhao Fang, Xuanlin Li, Zhiao Huang, Mingu Lee, Roland Memisevic, Hao Su | cs.CL, cs.AI, cs.LG | Published at NeurIPS 2023 | null | cs.CL | 20230606 | 20231003 | 3 2 0 2
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# Deductive Verification of Chain-of-Thought Reasoning
Zhan Ling1â Yunhao Fang1â Xuanlin Li1 Zhiao Huang1 Mingu Lee2 Roland Memisevic2 Hao Su1 1UC San Diego, 2Qualcomm AI Researchâ
# Abstract
Large Language Models (LLMs) significantly benefit from ... | {
"id": "2302.13971"
} |
2306.03604 | Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach | Large language models (LLMs) encode a vast amount of world knowledge acquired
from massive text datasets. Recent studies have demonstrated that LLMs can
assist an embodied agent in solving complex sequential decision making tasks by
providing high-level instructions. However, interactions with LLMs can be
time-consumin... | http://arxiv.org/pdf/2306.03604 | Bin Hu, Chenyang Zhao, Pu Zhang, Zihao Zhou, Yuanhang Yang, Zenglin Xu, Bin Liu | cs.AI | 12 pages | null | cs.AI | 20230606 | 20230831 | 3 2 0 2
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# Enabling Intelligent Interactions between an Agent and an LLM: A Reinforcement Learning Approach
Bin Hu, Chenyang Zhao, Pu Zhang, Zihao Zhou, Yuanhang Yang, Zenglin Xu, Bin Liu
AbstractâLarge language models (LLMs) encode a vast amount of world kno... | {
"id": "2302.13971"
} |
2306.02841 | CTRL: Connect Collaborative and Language Model for CTR Prediction | Traditional click-through rate (CTR) prediction models convert the tabular
data into one-hot vectors and leverage the collaborative relations among
features for inferring the user's preference over items. This modeling paradigm
discards essential semantic information. Though some works like P5 and CTR-BERT
have explore... | http://arxiv.org/pdf/2306.02841 | Xiangyang Li, Bo Chen, Lu Hou, Ruiming Tang | cs.IR | null | null | cs.IR | 20230605 | 20231218 | 3 2 0 2
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# CTRL: Connect Collaborative and Language Model for CTR Prediction
Xiangyang Liâ lixiangyang34@huawei.com China Huawei Noahâs Ark Lab
# Bo Chenâ chenbo116@huawei.com China Huawei Noahâs Ark Lab
# Lu Hou houlu3@huawei.com China Huawei Noahâs ... | {
"id": "1810.04805"
} |
2306.02549 | Evaluation of AI Chatbots for Patient-Specific EHR Questions | This paper investigates the use of artificial intelligence chatbots for
patient-specific question answering (QA) from clinical notes using several
large language model (LLM) based systems: ChatGPT (versions 3.5 and 4), Google
Bard, and Claude. We evaluate the accuracy, relevance, comprehensiveness, and
coherence of the... | http://arxiv.org/pdf/2306.02549 | Alaleh Hamidi, Kirk Roberts | cs.CL, cs.AI, cs.IR | null | null | cs.CL | 20230605 | 20230605 | 3 2 0 2
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# Evaluation of AI Chatbots for Patient-Specific EHR Questions
Alaleh Hamidi, Kirk Roberts McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston {alaleh.hamidi,kirk.roberts}@uth.tmc.edu
# Abstract
This paper... | {
"id": "1805.06816"
} |
2306.02552 | When Large Language Model based Agent Meets User Behavior Analysis: A Novel User Simulation Paradigm | User behavior analysis is crucial in human-centered AI applications. In this
field, the collection of sufficient and high-quality user behavior data has
always been a fundamental yet challenging problem. An intuitive idea to address
this problem is automatically simulating the user behaviors. However, due to
the subjec... | http://arxiv.org/pdf/2306.02552 | Lei Wang, Jingsen Zhang, Hao Yang, Zhiyuan Chen, Jiakai Tang, Zeyu Zhang, Xu Chen, Yankai Lin, Ruihua Song, Wayne Xin Zhao, Jun Xu, Zhicheng Dou, Jun Wang, Ji-Rong Wen | cs.IR, cs.AI | 26 pages, 9 figures | null | cs.IR | 20230605 | 20230918 | 3 2 0 2
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# When Large Language Model based Agent Meets User Behavior Analysis: A Novel User Simulation Paradigm
Lei Wang1, Jingsen Zhang1, Hao Yang1, Zhiyuan Chen1, Jiakai Tang1, Zeyu Zhang1, Xu Chen1, Yankai Lin1, Ruihua Song1, Wayne Xin Zhao1, Jun Xu1, Zhiche... | {
"id": "2109.08331"
} |
2306.02707 | Orca: Progressive Learning from Complex Explanation Traces of GPT-4 | Recent research has focused on enhancing the capability of smaller models
through imitation learning, drawing on the outputs generated by large
foundation models (LFMs). A number of issues impact the quality of these
models, ranging from limited imitation signals from shallow LFM outputs; small
scale homogeneous traini... | http://arxiv.org/pdf/2306.02707 | Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, Ahmed Awadallah | cs.CL, cs.LG | null | null | cs.CL | 20230605 | 20230605 | 3 2 0 2 n u J 5 ] L C . s c [
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ae
# Orca: Progressive Learning from Complex Explanation Traces of GPT-4
Subhabrata Mukherjeeââ , Arindam Mitraâ
Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, Ahmed Awadallah
Microsoft Research
# Abstract
Recent research has focused on enhancing the... | {
"id": "2302.13971"
} |
2306.02858 | Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding | We present Video-LLaMA a multi-modal framework that empowers Large Language
Models (LLMs) with the capability of understanding both visual and auditory
content in the video. Video-LLaMA bootstraps cross-modal training from the
frozen pre-trained visual and audio encoders and the frozen LLMs. Unlike
previous works that ... | http://arxiv.org/pdf/2306.02858 | Hang Zhang, Xin Li, Lidong Bing | cs.CL, cs.CV, cs.SD, eess.AS | Accepted by EMNLP 2023's demo track; Code, Pretrained Model, and
Dataset: https://github.com/DAMO-NLP-SG/Video-LLaMA | null | cs.CL | 20230605 | 20231025 | 3 2 0 2
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BE)
# Video-LLaMA
# An Instruction-tuned Audio-Visual Language Model for Video Understanding
Xin Li1 2â 1 DAMO Academy, Alibaba Group 2 Hupan Lab, 310023, Hangzhou, China {zh401075, xinting.lx, l.bing}@alibaba-inc.com
# Abstract
We present Video-LLaM... | {
"id": "2306.05424"
} |
2306.03090 | Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction | Coaching, which involves classroom observation and expert feedback, is a
widespread and fundamental part of teacher training. However, the majority of
teachers do not have access to consistent, high quality coaching due to limited
resources and access to expertise. We explore whether generative AI could
become a cost-e... | http://arxiv.org/pdf/2306.03090 | Rose E. Wang, Dorottya Demszky | cs.CL, cs.AI | In the Proceedings of Innovative Use of NLP for Building Educational
Applications 2023; The code and model outputs are open-sourced here:
https://github.com/rosewang2008/zero-shot-teacher-feedback | null | cs.CL | 20230605 | 20230605 | 3 2 0 2
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# Is ChatGPT a Good Teacher Coach? Measuring Zero-Shot Performance For Scoring and Providing Actionable Insights on Classroom Instruction
# Rose Wang rewang@cs.stanford.edu Stanford University
# Dorottya Demszky ddemszky@stanford.edu Stanford University
... | {
"id": "2211.11772"
} |
2306.03314 | Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents | In this paper, we present a novel framework for enhancing the capabilities of
large language models (LLMs) by leveraging the power of multi-agent systems.
Our framework introduces a collaborative environment where multiple intelligent
agent components, each with distinctive attributes and roles, work together to
handle... | http://arxiv.org/pdf/2306.03314 | Yashar Talebirad, Amirhossein Nadiri | cs.AI, cs.LG, cs.MA | null | null | cs.AI | 20230605 | 20230605 | 3 2 0 2 n u J 5 ] I A . s c [
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# MULTI-AGENT COLLABORATION: HARNESSING THE POWER OF INTELLIGENT LLM AGENTS
Yashar Talebirad University of Alberta Edmonton, Alberta, Canada talebira@ualberta.ca
Amirhossein Nadiri York University Toronto, Ontaria, Canada anadiri@yorku.ca
# ABSTRACT
In t... | {
"id": "2302.13971"
} |
2306.03078 | SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression | Recent advances in large language model (LLM) pretraining have led to
high-quality LLMs with impressive abilities. By compressing such LLMs via
quantization to 3-4 bits per parameter, they can fit into memory-limited
devices such as laptops and mobile phones, enabling personalized use. However,
quantization down to 3-4... | http://arxiv.org/pdf/2306.03078 | Tim Dettmers, Ruslan Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, Dan Alistarh | cs.CL, cs.LG | Extended preprint | null | cs.CL | 20230605 | 20230605 | 3 2 0 2 n u J 5 ] L C . s c [
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# SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
Tim Dettmersââ University of Washington Ruslan Svirschevskiâ HSE University & Yandex Vage Egiazarianâ HSE University & Yandex Elias Frantar IST Austria Saleh Ashkb... | {
"id": "2302.13971"
} |
2306.02408 | Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning | Chain-of-thought prompting~(CoT) and tool augmentation have been validated in
recent work as effective practices for improving large language models~(LLMs)
to perform step-by-step reasoning on complex math-related tasks. However, most
existing math reasoning datasets may be not able to fully evaluate and analyze
the ab... | http://arxiv.org/pdf/2306.02408 | Beichen Zhang, Kun Zhou, Xilin Wei, Wayne Xin Zhao, Jing Sha, Shijin Wang, Ji-Rong Wen | cs.CL | 17 pages, working in progress | null | cs.CL | 20230604 | 20230604 | 3 2 0 2 n u J 4 ] L C . s c [
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# Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning
Beichen Zhang13â, Kun Zhou23â, Xilin Wei4, Wayne Xin Zhao13â , Jing Sha5, Shijin Wang56, Ji-Rong Wen123 1Gaoling School of Artificial Intelligence, Renmin University of C... | {
"id": "2205.12255"
} |
2306.01987 | Prompting Is All You Need: Automated Android Bug Replay with Large Language Models | Bug reports are vital for software maintenance that allow users to inform
developers of the problems encountered while using the software. As such,
researchers have committed considerable resources toward automating bug replay
to expedite the process of software maintenance. Nonetheless, the success of
current automate... | http://arxiv.org/pdf/2306.01987 | Sidong Feng, Chunyang Chen | cs.SE | Accepted to 46th International Conference on Software Engineering
(ICSE 2024) | null | cs.SE | 20230603 | 20230718 | 3 2 0 2
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# Prompting Is All You Need: Automated Android Bug Replay with Large Language Models
Sidong Feng Monash University Melbourne, Australia sidong.feng@monash.edu
Chunyang Chen Monash University Melbourne, Australia chunyang.chen@monash.edu
ABSTRACT Bug re... | {
"id": "1907.11692"
} |
2306.01499 | Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today | Recent investigations show that large language models (LLMs), specifically
GPT-4, not only have remarkable capabilities in common Natural Language
Processing (NLP) tasks but also exhibit human-level performance on various
professional and academic benchmarks. However, whether GPT-4 can be directly
used in practical app... | http://arxiv.org/pdf/2306.01499 | Zhuo Wang, Rongzhen Li, Bowen Dong, Jie Wang, Xiuxing Li, Ning Liu, Chenhui Mao, Wei Zhang, Liling Dong, Jing Gao, Jianyong Wang | cs.CL, cs.LG | 16 pages, 6 figures | null | cs.CL | 20230602 | 20230602 | 3 2 0 2 n u J 2 ] L C . s c [
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# Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today
Zhuo Wang1, Rongzhen Li2, Bowen Dong1, Jie Wang3, Xiuxing Li4,5, Ning Liu7, Chenhui Mao2, Wei Zhang6, Liling Dong2, Jing Gao2â, Jianyong Wang1ââ 1Depa... | {
"id": "1810.04805"
} |
2306.01694 | Evaluating Language Models for Mathematics through Interactions | There is much excitement about the opportunity to harness the power of large
language models (LLMs) when building problem-solving assistants. However, the
standard methodology of evaluating LLMs relies on static pairs of inputs and
outputs, and is insufficient for making an informed decision about which LLMs
and under ... | http://arxiv.org/pdf/2306.01694 | Katherine M. Collins, Albert Q. Jiang, Simon Frieder, Lionel Wong, Miri Zilka, Umang Bhatt, Thomas Lukasiewicz, Yuhuai Wu, Joshua B. Tenenbaum, William Hart, Timothy Gowers, Wenda Li, Adrian Weller, Mateja Jamnik | cs.LG, cs.HC | null | null | cs.LG | 20230602 | 20231105 | 3 2 0 2
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# Evaluating Language Models for Mathematics through Interactions
Katherine M. Collinsâa, Albert Q. Jiangâa, Simon Friederb, Lionel Wongc, Miri Zilkaa, Umang Bhatta,d,e, Thomas Lukasiewiczf,b, Yuhuai Wuâ g, Joshua B. Tenenbaumc, William Harta, Tim... | {
"id": "2210.09150"
} |
2306.01693 | Fine-Grained Human Feedback Gives Better Rewards for Language Model Training | Language models (LMs) often exhibit undesirable text generation behaviors,
including generating false, toxic, or irrelevant outputs. Reinforcement
learning from human feedback (RLHF) - where human preference judgments on LM
outputs are transformed into a learning signal - has recently shown promise in
addressing these ... | http://arxiv.org/pdf/2306.01693 | Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A. Smith, Mari Ostendorf, Hannaneh Hajishirzi | cs.CL | NeurIPS 2023 camera-ready | null | cs.CL | 20230602 | 20231030 | 3 2 0 2
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# Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
Zeqiu Wu1â Yushi Hu1â Weijia Shi1 Nouha Dziri2 Alane Suhr3 Prithviraj Ammanabrolu45 Noah A. Smith12 Mari Ostendorf1 Hannaneh Hajishirzi12 1University of Washington 3Univ... | {
"id": "2305.14251"
} |
2306.01590 | Log Parsing: How Far Can ChatGPT Go? | Software logs play an essential role in ensuring the reliability and
maintainability of large-scale software systems, as they are often the sole
source of runtime information. Log parsing, which converts raw log messages
into structured data, is an important initial step towards downstream log
analytics. In recent stud... | http://arxiv.org/pdf/2306.01590 | Van-Hoang Le, Hongyu Zhang | cs.SE, cs.AI | This paper is accepted by ASE 2023, NIER Track | null | cs.SE | 20230602 | 20230820 | 3 2 0 2
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# Log Parsing: How Far Can ChatGPT Go?
Van-Hoang Le1 and Hongyu Zhang2â 1School of Information and Physical Sciences, The University of Newcastle, Australia 2School of Big Data and Software Engineering, Chongqing University, China vanhoang.le@uon.edu... | {
"id": "1611.03213"
} |
2306.01337 | An Empirical Study on Challenging Math Problem Solving with GPT-4 | Employing Large Language Models (LLMs) to address mathematical problems is an
intriguing research endeavor, considering the abundance of math problems
expressed in natural language across numerous science and engineering fields.
While several prior works have investigated solving elementary mathematics
using LLMs, this... | http://arxiv.org/pdf/2306.01337 | Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, Chi Wang | cs.CL, stat.ML | Fix minor errors, update github link | null | cs.CL | 20230602 | 20230608 | 3 2 0 2
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# An Empirical Study on Challenging Math Problem Solving with GPT-4
# Yiran Wu Pennsylvania State University yiran.wu@psu.edu
# Feiran Jia Pennsylvania State University feiran.jia@psu.edu
# Shaokun Zhang Pennsylvania State University shaokun.zhang@psu.ed... | {
"id": "2206.02336"
} |
2306.01248 | How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization? | Automatic summarization of legal case judgements has traditionally been
attempted by using extractive summarization methods. However, in recent years,
abstractive summarization models are gaining popularity since they can generate
more natural and coherent summaries. Legal domain-specific pre-trained
abstractive summar... | http://arxiv.org/pdf/2306.01248 | Aniket Deroy, Kripabandhu Ghosh, Saptarshi Ghosh | cs.CL, cs.IR, cs.LG | Accepted for presentation at the 3rd Workshop on Artificial
Intelligence and Intelligent Assistance for Legal Professionals in the
Digital Workplace (LegalAIIA 2023), co-located with the ICAIL 2023 conference | null | cs.CL | 20230602 | 20230614 | 3 2 0 2 n u J 4 1 ] L C . s c [
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# How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization?
Kripabandhu Ghosh IISER Kolkata West Bengal 741246, India kripa.ghosh@gmail.com
# Aniket Deroy IIT Kharagpur West Bengal 721302, India roydanik18@gmail.com
... | {
"id": "2112.14168"
} |
2306.00978 | AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration | Large language models (LLMs) have shown excellent performance on various
tasks, but the astronomical model size raises the hardware barrier for serving
(memory size) and slows down token generation (memory bandwidth). In this
paper, we propose Activation-aware Weight Quantization (AWQ), a
hardware-friendly approach for... | http://arxiv.org/pdf/2306.00978 | Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Xingyu Dang, Chuang Gan, Song Han | cs.CL | Code available at: https://github.com/mit-han-lab/llm-awq | null | cs.CL | 20230601 | 20231003 | 3 2 0 2
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# AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
# Ji Lin1â Jiaming Tang1,2â Haotian Tang1 Shang Yang1 Xingyu Dang3 Chuang Gan1 Song Han1 1MIT 2SJTU 3 Tsinghua University
# https://github.com/mit-han-lab/llm-awq
# Abst... | {
"id": "2102.05426"
} |
2306.00890 | LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day | Conversational generative AI has demonstrated remarkable promise for
empowering biomedical practitioners, but current investigations focus on
unimodal text. Multimodal conversational AI has seen rapid progress by
leveraging billions of image-text pairs from the public web, but such
general-domain vision-language models... | http://arxiv.org/pdf/2306.00890 | Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, Jianfeng Gao | cs.CV, cs.CL | 17 pages; Website: https://aka.ms/llava-med | null | cs.CV | 20230601 | 20230601 | 3 2 0 2 n u J 1 ] V C . s c [
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# LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day
Chunyuan Liâ, Cliff Wongâ, Sheng Zhangâ, Naoto Usuyama, Haotian Liu, Jianwei Yang Tristan Naumann, Hoifung Poon, Jianfeng Gao Microsoft https://aka.ms/llava-med
... | {
"id": "2303.00915"
} |
2306.00924 | Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker | Theory of Mind (ToM)$\unicode{x2014}$the ability to reason about the mental
states of other people$\unicode{x2014}$is a key element of our social
intelligence. Yet, despite their ever more impressive performance, large-scale
neural language models still lack basic theory of mind capabilities
out-of-the-box. We posit th... | http://arxiv.org/pdf/2306.00924 | Melanie Sclar, Sachin Kumar, Peter West, Alane Suhr, Yejin Choi, Yulia Tsvetkov | cs.CL, cs.AI, cs.LG | null | ACL 2023 | cs.CL | 20230601 | 20230601 | 3 2 0 2
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# Minding Language Modelsâ (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker
Sachin Kumar2 Yejin Choi1,3 Yulia Tsvetkov1 1Paul G. Allen School of Computer Science & Engineering, University of Washington 2Language Technologies Ins... | {
"id": "2212.08286"
} |
2306.00937 | STEVE-1: A Generative Model for Text-to-Behavior in Minecraft | Constructing AI models that respond to text instructions is challenging,
especially for sequential decision-making tasks. This work introduces an
instruction-tuned Video Pretraining (VPT) model for Minecraft called STEVE-1,
demonstrating that the unCLIP approach, utilized in DALL-E 2, is also effective
for creating ins... | http://arxiv.org/pdf/2306.00937 | Shalev Lifshitz, Keiran Paster, Harris Chan, Jimmy Ba, Sheila McIlraith | cs.AI, cs.LG | null | null | cs.AI | 20230601 | 20230605 | 3 2 0 2 n u J 5 ] I A . s c [
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# STEVE-1: A Generative Model for Text-to-Behavior in Minecraft
# Shalev Lifshitz1,2â shalev.lifshitz@mail.utoronto.ca
# Keiran Paster1,2â keirp@cs.toronto.edu
# Harris Chan1,2â hchan@cs.toronto.edu
Sheila McIlraith1,2 sheila@cs.toronto.edu
Jimmy B... | {
"id": "2206.01079"
} |
2306.01116 | The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only | Large language models are commonly trained on a mixture of filtered web data
and curated high-quality corpora, such as social media conversations, books, or
technical papers. This curation process is believed to be necessary to produce
performant models with broad zero-shot generalization abilities. However, as
larger ... | http://arxiv.org/pdf/2306.01116 | Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, Julien Launay | cs.CL, cs.AI | null | null | cs.CL | 20230601 | 20230601 | 3 2 0 2 n u J 1 ] L C . s c [
1 v 6 1 1 1 0 . 6 0 3 2 : v i X r a
# The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only
# The Falcon LLM team
Guilherme Penedo 1 Quentin Malartic 2 Daniel Hesslow 1 Ruxandra Cojocaru 2 Alessandro Cappelli 1 Hamza Alobeidli 2 Baptiste Pann... | {
"id": "2302.13971"
} |
2305.19860 | A Survey on Large Language Models for Recommendation | Large Language Models (LLMs) have emerged as powerful tools in the field of
Natural Language Processing (NLP) and have recently gained significant
attention in the domain of Recommendation Systems (RS). These models, trained
on massive amounts of data using self-supervised learning, have demonstrated
remarkable success... | http://arxiv.org/pdf/2305.19860 | Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, Hui Xiong, Enhong Chen | cs.IR, cs.AI | 13 pages, 3 figures | null | cs.IR | 20230531 | 20230818 | 3 2 0 2
g u A 8 1
] R I . s c [ 4 v 0 6 8 9 1 . 5 0 3 2 : v i X r a
# A Survey on Large Language Models for Recommendation
Likang Wu1,2â , Zhi Zheng1,2â , Zhaopeng Qiu2â , Hao Wang1â , Hongchao Gu1 , Tingjia Shen1 , Chuan Qin2 , Chen Zhu2 , Hengshu Zhu2â , Qi Liu1 , Hui Xiong3â , Enhong Chen1â 1University... | {
"id": "2104.08786"
} |
2305.19534 | Recasting Self-Attention with Holographic Reduced Representations | In recent years, self-attention has become the dominant paradigm for sequence
modeling in a variety of domains. However, in domains with very long sequence
lengths the $\mathcal{O}(T^2)$ memory and $\mathcal{O}(T^2 H)$ compute costs
can make using transformers infeasible. Motivated by problems in malware
detection, whe... | http://arxiv.org/pdf/2305.19534 | Mohammad Mahmudul Alam, Edward Raff, Stella Biderman, Tim Oates, James Holt | cs.LG, cs.AI, stat.ML | To appear in Proceedings of the 40th International Conference on
Machine Learning (ICML) | null | cs.LG | 20230531 | 20230531 | 3 2 0 2
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# Recasting Self-Attention with Holographic Reduced Representations
# Mohammad Mahmudul Alam 1 Edward Raff 1 2 3 Stella Biderman 2 3 4 Tim Oates 1 James Holt 2
# Abstract
In recent years, self-attention has become the dominant paradigm for sequence mod... | {
"id": "2004.05150"
} |
2305.20050 | Let's Verify Step by Step | In recent years, large language models have greatly improved in their ability
to perform complex multi-step reasoning. However, even state-of-the-art models
still regularly produce logical mistakes. To train more reliable models, we can
turn either to outcome supervision, which provides feedback for a final result,
or ... | http://arxiv.org/pdf/2305.20050 | Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe | cs.LG, cs.AI, cs.CL | null | null | cs.LG | 20230531 | 20230531 | 3 2 0 2
y a M 1 3 ] G L . s c [
1 v 0 5 0 0 2 . 5 0 3 2 : v i X r a
# Letâs Verify Step by Step
# Hunter Lightmanâ
â-
# Vineet Kosarajuâ
# Yura Burdaâ
# Harri Edwards
Bowen Baker Teddy Lee Jan Leike John Schulman Ilya Sutskever
# Karl Cobbeâ
OpenAI
# Abstract
In recent years, large language models have grea... | {
"id": "2206.02336"
} |
2305.20076 | Decision-Oriented Dialogue for Human-AI Collaboration | We describe a class of tasks called decision-oriented dialogues, in which AI
assistants must collaborate with one or more humans via natural language to
help them make complex decisions. We formalize three domains in which users
face everyday decisions: (1) choosing an assignment of reviewers to conference
papers, (2) ... | http://arxiv.org/pdf/2305.20076 | Jessy Lin, Nicholas Tomlin, Jacob Andreas, Jason Eisner | cs.CL, cs.AI | null | null | cs.CL | 20230531 | 20230601 | 3 2 0 2 n u J 1 ] L C . s c [
2 v 6 7 0 0 2 . 5 0 3 2 : v i X r a
# Decision-Oriented Dialogue for HumanâAI Collaboration
# Jason Eisner2 4 4 Johns Hopkins
Jacob Andreas2 3 3 MIT Nicholas Tomlin
# Jessy Lin
# Abstract
We describe a class of tasks called decision- oriented dialogues, in which AI assistants must collab... | {
"id": "2302.13971"
} |
2306.00245 | From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces | Much of the previous work towards digital agents for graphical user
interfaces (GUIs) has relied on text-based representations (derived from HTML
or other structured data sources), which are not always readily available.
These input representations have been often coupled with custom, task-specific
action spaces. This ... | http://arxiv.org/pdf/2306.00245 | Peter Shaw, Mandar Joshi, James Cohan, Jonathan Berant, Panupong Pasupat, Hexiang Hu, Urvashi Khandelwal, Kenton Lee, Kristina Toutanova | cs.LG, cs.CL, cs.CV, cs.HC | null | null | cs.LG | 20230531 | 20231206 | 3 2 0 2
c e D 6 ] G L . s c [
2 v 5 4 2 0 0 . 6 0 3 2 : v i X r a
# From Pixels to UI Actions: Learning to Follow Instructions via Graphical User Interfaces
Peter Shaw1â Mandar Joshi1â James Cohan2 Jonathan Berant1 Panupong Pasupat1 Hexiang Hu1 Urvashi Khandelwal1
# Kristina Toutanova1
1 Google DeepMind 2 Google
# ... | {
"id": "2303.07280"
} |
2305.19308 | SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models | Computer end users have spent billions of hours completing daily tasks like
tabular data processing and project timeline scheduling. Most of these tasks
are repetitive and error-prone, yet most end users lack the skill to automate
these burdensome works. With the advent of large language models (LLMs),
directing softwa... | http://arxiv.org/pdf/2305.19308 | Hongxin Li, Jingran Su, Yuntao Chen, Qing Li, Zhaoxiang Zhang | cs.SE, cs.AI, cs.CL | Accepted to NeurIPS 2023 | null | cs.SE | 20230530 | 20231030 | 3 2 0 2
t c O 0 3 ] E S . s c [
2 v 8 0 3 9 1 . 5 0 3 2 : v i X r a
# SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models
Hongxin Liâ1,2, Jingran Suâ3,4, Yuntao Chenâ 3, Qing Liâ 4, and Zhaoxiang Zhangâ 1,2,3,5
1School of Artificial Intelligence, University of Chinese Aca... | {
"id": "2303.00855"
} |
2305.19118 | Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate | Modern large language models (LLMs) like ChatGPT have shown remarkable
performance on general language tasks but still struggle on complex reasoning
tasks, which drives the research on cognitive behaviors of LLMs to explore
human-like problem-solving strategies. Along this direction, one representative
strategy is self... | http://arxiv.org/pdf/2305.19118 | Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Zhaopeng Tu, Shuming Shi | cs.CL | Work in progress | null | cs.CL | 20230530 | 20230530 | 3 2 0 2
y a M 0 3 ] L C . s c [
1 v 8 1 1 9 1 . 5 0 3 2 : v i X r a
# Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate
# Tian Liang1â Zhiwei He2â Wenxiang Jiao3â Xing Wang3â Yan Wang Rui Wang2 Yujiu Yang1â Zhaopeng Tu3 Shuming Shi3
1Tsinghua Shenzhen International Graduate Sc... | {
"id": "2305.14221"
} |
2305.18290 | Direct Preference Optimization: Your Language Model is Secretly a Reward Model | While large-scale unsupervised language models (LMs) learn broad world
knowledge and some reasoning skills, achieving precise control of their
behavior is difficult due to the completely unsupervised nature of their
training. Existing methods for gaining such steerability collect human labels
of the relative quality of... | http://arxiv.org/pdf/2305.18290 | Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, Chelsea Finn | cs.LG, cs.AI, cs.CL | null | null | cs.LG | 20230529 | 20231213 | 3 2 0 2
c e D 3 1 ] G L . s c [
2 v 0 9 2 8 1 . 5 0 3 2 : v i X r a
# Direct Preference Optimization: Your Language Model is Secretly a Reward Model
# Rafael Rafailovââ
# Archit Sharmaââ
# Eric Mitchellââ
# Stefano Ermonâ â¡
# Christopher D. Manningâ
# Chelsea Finnâ
â Stanford University â¡CZ Biohub... | {
"id": "2302.13971"
} |
2305.18279 | Contextual Object Detection with Multimodal Large Language Models | Recent Multimodal Large Language Models (MLLMs) are remarkable in
vision-language tasks, such as image captioning and question answering, but
lack the essential perception ability, i.e., object detection. In this work, we
address this limitation by introducing a novel research problem of contextual
object detection -- ... | http://arxiv.org/pdf/2305.18279 | Yuhang Zang, Wei Li, Jun Han, Kaiyang Zhou, Chen Change Loy | cs.CV, cs.AI | Github: https://github.com/yuhangzang/ContextDET, Project Page:
https://www.mmlab-ntu.com/project/contextdet/index.html | null | cs.CV | 20230529 | 20230529 | 3 2 0 2
y a M 9 2 ] V C . s c [
1 v 9 7 2 8 1 . 5 0 3 2 : v i X r a
# Contextual Object Detection with Multimodal Large Language Models
Yuhang Zang, Wei Li, Jun Han, Kaiyang Zhou, Chen Change Loy⢠S-Lab, Nanyang Technological University {zang0012, wei.1, hanj0030, kaiyang.zhou, ccloy}@ntu.edu.sg
# Abstract
Recent Mul... | {
"id": "2302.13971"
} |
2305.18654 | Faith and Fate: Limits of Transformers on Compositionality | Transformer large language models (LLMs) have sparked admiration for their
exceptional performance on tasks that demand intricate multi-step reasoning.
Yet, these models simultaneously show failures on surprisingly trivial
problems. This begs the question: Are these errors incidental, or do they
signal more substantial... | http://arxiv.org/pdf/2305.18654 | Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang Lorraine Li, Liwei Jiang, Bill Yuchen Lin, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena D. Hwang, Soumya Sanyal, Sean Welleck, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, Yejin Choi | cs.CL, cs.AI, cs.LG | 10 pages + appendix (40 pages) | null | cs.CL | 20230529 | 20231031 | 3 2 0 2
t c O 1 3 ] L C . s c [
3 v 4 5 6 8 1 . 5 0 3 2 : v i X r a
# Faith and Fate: Limits of Transformers on Compositionality
# Nouha Dziri1 Xiang Lorraine Li1
# â, Ximing Lu1,2
# â, Melanie Sclar2
â,
# â , Liwei Jiang1,2 Peter West1,2, Chandra Bhagavatula1, Ronan Le Bras1, Jena D. Hwang1, Soumya Sanyal3, Sea... | {
"id": "2305.00061"
} |
2305.18565 | PaLI-X: On Scaling up a Multilingual Vision and Language Model | We present the training recipe and results of scaling up PaLI-X, a
multilingual vision and language model, both in terms of size of the components
and the breadth of its training task mixture. Our model achieves new levels of
performance on a wide-range of varied and complex tasks, including multiple
image-based captio... | http://arxiv.org/pdf/2305.18565 | Xi Chen, Josip Djolonga, Piotr Padlewski, Basil Mustafa, Soravit Changpinyo, Jialin Wu, Carlos Riquelme Ruiz, Sebastian Goodman, Xiao Wang, Yi Tay, Siamak Shakeri, Mostafa Dehghani, Daniel Salz, Mario Lucic, Michael Tschannen, Arsha Nagrani, Hexiang Hu, Mandar Joshi, Bo Pang, Ceslee Montgomery, Paulina Pietrzyk, Marvin... | cs.CV, cs.CL, cs.LG | null | null | cs.CV | 20230529 | 20230529 | 3 2 0 2
y a M 9 2 ] V C . s c [
1 v 5 6 5 8 1 . 5 0 3 2 : v i X r a
# PaLI-X: On Scaling up a Multilingual Vision and Language Model
Xi Chen, Josip Djolonga, Piotr Padlewski, Basil Mustafa, Soravit Changpinyo, Jialin Wu, Carlos Riquelme Ruiz, Sebastian Goodman, Xiao Wang, Yi Tay, Siamak Shakeri, Mostafa Dehghani, Danie... | {
"id": "2302.11154"
} |
2305.18486 | A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets | The development of large language models (LLMs) such as ChatGPT has brought a
lot of attention recently. However, their evaluation in the benchmark academic
datasets remains under-explored due to the difficulty of evaluating the
generative outputs produced by this model against the ground truth. In this
paper, we aim t... | http://arxiv.org/pdf/2305.18486 | Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, Md Amran Hossen Bhuiyan, Shafiq Joty, Jimmy Xiangji Huang | cs.CL, cs.AI, cs.LG | Accepted by ACL 2023 Findings. The first three authors contributed
equally | null | cs.CL | 20230529 | 20230705 | 3 2 0 2
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4 v 6 8 4 8 1 . 5 0 3 2 : v i X r a
# A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets
Md Tahmid Rahman Laskarââ §, M Saiful Bariââ¡, Mizanur Rahmanââ ¶ Md Amran Hossen Bhuiyanâ , Shaï¬q Jotyâ¡$, Jimmy Xiangji Huangâ â York University, â¡... | {
"id": "2302.13971"
} |
2305.17926 | Large Language Models are not Fair Evaluators | In this paper, we uncover a systematic bias in the evaluation paradigm of
adopting large language models~(LLMs), e.g., GPT-4, as a referee to score and
compare the quality of responses generated by candidate models. We find that
the quality ranking of candidate responses can be easily hacked by simply
altering their or... | http://arxiv.org/pdf/2305.17926 | Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Qi Liu, Tianyu Liu, Zhifang Sui | cs.CL, cs.AI, cs.IR | null | null | cs.CL | 20230529 | 20230830 | 3 2 0 2
g u A 0 3 ] L C . s c [
2 v 6 2 9 7 1 . 5 0 3 2 : v i X r a
# Large Language Models are not Fair Evaluators
Peiyi Wang1 Lei Li1 Liang Chen1 Zefan Cai1 Dawei Zhu1 Binghuai Lin3 Yunbo Cao3 Qi Liu2 Tianyu Liu3 Zhifang Sui1 1 National Key Laboratory for Multimedia Information Processing, Peking University 2 The Uni... | {
"id": "2306.05685"
} |
2305.18098 | BigTranslate: Augmenting Large Language Models with Multilingual Translation Capability over 100 Languages | Large language models (LLMs) demonstrate promising translation performance
among various natural languages. However, many LLMs especially the open-sourced
ones, such as BLOOM and LLaMA, are English-dominant and support only dozens of
natural languages, making the potential of LLMs on language translation less
explored.... | http://arxiv.org/pdf/2305.18098 | Wen Yang, Chong Li, Jiajun Zhang, Chengqing Zong | cs.CL | 16 pages, 4 figures. Our model is available at
https://github.com/ZNLP/BigTranslate | null | cs.CL | 20230529 | 20231121 | 3 2 0 2
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# BigTranslate: Augmenting Large Language Models with Multilingual Translation Capability over 100 Languages
Wen Yang1,2, Chong Li1,2, Jiajun Zhang1,2,3â, and Chengqing Zong1,2 1 Institute of Automation, Chinese Academy of Sciences 2 School of Artifi... | {
"id": "2304.04675"
} |
2305.17608 | Reward Collapse in Aligning Large Language Models | The extraordinary capabilities of large language models (LLMs) such as
ChatGPT and GPT-4 are in part unleashed by aligning them with reward models
that are trained on human preferences, which are often represented as rankings
of responses to prompts. In this paper, we document the phenomenon of
\textit{reward collapse}... | http://arxiv.org/pdf/2305.17608 | Ziang Song, Tianle Cai, Jason D. Lee, Weijie J. Su | cs.LG, cs.AI, cs.CL, math.OC, stat.ML | null | null | cs.LG | 20230528 | 20230528 | 3 2 0 2
y a M 8 2 ] G L . s c [
1 v 8 0 6 7 1 . 5 0 3 2 : v i X r a
Reward Collapse in Aligning Large Language Models
# Ziang Song*
# Tianle Caiâ
# Jason D. Leeâ
# Weijie J. Suâ¡
May 25, 2023
# Abstract
The extraordinary capabilities of large language models (LLMs) such as ChatGPT and GPT-4 are in part unleashed b... | {
"id": "1909.08593"
} |
2305.17493 | The Curse of Recursion: Training on Generated Data Makes Models Forget | Stable Diffusion revolutionised image creation from descriptive text. GPT-2,
GPT-3(.5) and GPT-4 demonstrated astonishing performance across a variety of
language tasks. ChatGPT introduced such language models to the general public.
It is now clear that large language models (LLMs) are here to stay, and will
bring abou... | http://arxiv.org/pdf/2305.17493 | Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, Ross Anderson | cs.LG, cs.AI, cs.CL, cs.CR, cs.CV | null | null | cs.LG | 20230527 | 20230531 | 3 2 0 2
y a M 1 3 ] G L . s c [
2 v 3 9 4 7 1 . 5 0 3 2 : v i X r a
# THE CURSE OF RECURSION: TRAINING ON GENERATED DATA MAKES MODELS FORGET
# Ilia Shumailov* University of Oxford
# Zakhar Shumaylov* University of Cambridge
Yiren Zhao Imperial College London Yarin Gal University of Oxford
# Nicolas Papernot University ... | {
"id": "1810.04805"
} |
2305.18365 | What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks | Large Language Models (LLMs) with strong abilities in natural language
processing tasks have emerged and have been applied in various kinds of areas
such as science, finance and software engineering. However, the capability of
LLMs to advance the field of chemistry remains unclear. In this paper, rather
than pursuing s... | http://arxiv.org/pdf/2305.18365 | Taicheng Guo, Kehan Guo, Bozhao Nan, Zhenwen Liang, Zhichun Guo, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang | cs.CL, cs.AI | NeurIPS 2023 Datasets and Benchmarks Track camera-ready version | null | cs.CL | 20230527 | 20231228 | 3 2 0 2 c e D 8 2
] L C . s c [
3 v 5 6 3 8 1 . 5 0 3 2 : v i X r a
# What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks
Taicheng Guoâ, Kehan Guoâ, Bozhao Nan, Zhenwen Liang, Zhichun Guo, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhangâ University of Notre Dame {tguo2, kguo2, ... | {
"id": "2307.03109"
} |
2305.16960 | Training Socially Aligned Language Models on Simulated Social Interactions | Social alignment in AI systems aims to ensure that these models behave
according to established societal values. However, unlike humans, who derive
consensus on value judgments through social interaction, current language
models (LMs) are trained to rigidly replicate their training corpus in
isolation, leading to subpa... | http://arxiv.org/pdf/2305.16960 | Ruibo Liu, Ruixin Yang, Chenyan Jia, Ge Zhang, Denny Zhou, Andrew M. Dai, Diyi Yang, Soroush Vosoughi | cs.CL, cs.AI, cs.CY, cs.HC | Code, data, and models can be downloaded via
https://github.com/agi-templar/Stable-Alignment | null | cs.CL | 20230526 | 20231028 | 2023:
3 2 0 2
# t c O 8 2
] L C . s c [ 3 v 0 6 9 6 1 . 5 0 3 2 : v i X r a
TRAINING SOCIALLY ALIGNED LANGUAGE MODELS ON SIMULATED SOCIAL INTERACTIONS
# Ruibo Liu Google DeepMind
Ruixin Yang University of British Columbia
Chenyan Jia Stanford University
# Ge Zhang University of Michigan, Ann Arbor
Denny Zhou Google Dee... | {
"id": "1909.08593"
} |
2305.16675 | Multiview Identifiers Enhanced Generative Retrieval | Instead of simply matching a query to pre-existing passages, generative
retrieval generates identifier strings of passages as the retrieval target. At
a cost, the identifier must be distinctive enough to represent a passage.
Current approaches use either a numeric ID or a text piece (such as a title or
substrings) as t... | http://arxiv.org/pdf/2305.16675 | Yongqi Li, Nan Yang, Liang Wang, Furu Wei, Wenjie Li | cs.CL, cs.AI, cs.IR, cs.LG | ACL 2023 Main Conference | null | cs.CL | 20230526 | 20230526 | 3 2 0 2
y a M 6 2 ] L C . s c [
1 v 5 7 6 6 1 . 5 0 3 2 : v i X r a
# Multiview Identifiers Enhanced Generative Retrieval
Yongqi Li1, Nan Yang2, Liang Wang2, Furu Wei2, Wenjie Li1 1The Hong Kong Polytechnic University 2Microsoft liyongqi0@gmail.com {nanya,wangliang,fuwei}@microsoft.com cswjli@comp.polyu.edu.hk
# Abstra... | {
"id": "2207.02578"
} |
2305.16653 | AdaPlanner: Adaptive Planning from Feedback with Language Models | Large language models (LLMs) have recently demonstrated the potential in
acting as autonomous agents for sequential decision-making tasks. However, most
existing methods either take actions greedily without planning or rely on
static plans that are not adaptable to environmental feedback. Consequently,
the sequential d... | http://arxiv.org/pdf/2305.16653 | Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, Chao Zhang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230526 | 20230526 | 3 2 0 2
y a M 6 2 ] L C . s c [
1 v 3 5 6 6 1 . 5 0 3 2 : v i X r a
# AdaPlanner: Adaptive Planning from Feedback with Language Models
Haotian Sun1â, Yuchen Zhuang1ââ, Lingkai Kong1, Bo Dai1, Chao Zhang1 1 Georgia Institute of Technology
{haotian.sun, yczhuang, lkkong, chaozhang}@gatech.edu,
bodai@cc.gatech.edu
f... | {
"id": "2303.17580"
} |
2305.16744 | Demo2Code: From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought | Language instructions and demonstrations are two natural ways for users to
teach robots personalized tasks. Recent progress in Large Language Models
(LLMs) has shown impressive performance in translating language instructions
into code for robotic tasks. However, translating demonstrations into task code
continues to b... | http://arxiv.org/pdf/2305.16744 | Huaxiaoyue Wang, Gonzalo Gonzalez-Pumariega, Yash Sharma, Sanjiban Choudhury | cs.RO | 10 pages (not including references and appendix), 14 figures (7 in
main paper, 7 in appendix); (v3) camera-ready version | null | cs.RO | 20230526 | 20231102 | 3 2 0 2
v o N 2 ] O R . s c [
3 v 4 4 7 6 1 . 5 0 3 2 : v i X r a
# Demo2Code: From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought
# Huaxiaoyue Wang Cornell University yukiwang@cs.cornell.edu
Gonzalo Gonzalez-Pumariega Cornell University gg387@cornell.edu
Yash Sharma Cornell University ys... | {
"id": "2207.01780"
} |
2305.16837 | ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks | ChatGPT (Chat Generative Pre-trained Transformer) is a chatbot launched by
OpenAI on November 30, 2022. OpenAI's GPT-3 family of large language models
serve as the foundation for ChatGPT. ChatGPT is fine-tuned with both supervised
and reinforcement learning techniques and has received widespread attention for
its artic... | http://arxiv.org/pdf/2305.16837 | Giriprasad Sridhara, Ranjani H. G., Sourav Mazumdar | cs.SE, cs.AI, cs.LG | null | null | cs.SE | 20230526 | 20230526 | 3 2 0 2
y a M 6 2 ] E S . s c [
1 v 7 3 8 6 1 . 5 0 3 2 : v i X r a
# ChatGPT: A Study on its Utility for Ubiquitous Software Engineering Tasks
Ranjani H.G. Global AI Accelerator (GAIA) Ericsson Bangalore, India ranjani.h.g@ericsson.com
# Giriprasad Sridhara Global AI Accelerator (GAIA) Ericsson Bangalore, India giripr... | {
"id": "2012.08938"
} |
2305.16867 | Playing repeated games with Large Language Models | Large Language Models (LLMs) are transforming society and permeating into
diverse applications. As a result, LLMs will frequently interact with us and
other agents. It is, therefore, of great societal value to understand how LLMs
behave in interactive social settings. Here, we propose to use behavioral game
theory to s... | http://arxiv.org/pdf/2305.16867 | Elif Akata, Lion Schulz, Julian Coda-Forno, Seong Joon Oh, Matthias Bethge, Eric Schulz | cs.CL | null | null | cs.CL | 20230526 | 20230526 | 3 2 0 2
y a M 6 2 ] L C . s c [
1 v 7 6 8 6 1 . 5 0 3 2 : v i X r a
# Playing repeated games with Large Language Models
# Elif Akata1,â Seong Joon Oh1 Lion Schulz2 Julian Coda-Forno2 Eric Schulz2 Matthias Bethge1
# 1University of Tübingen
2Max Planck Institute for Biological Cybernetics, Tübingen â{elif.akata@uni... | {
"id": "2212.13371"
} |
2305.16934 | On Evaluating Adversarial Robustness of Large Vision-Language Models | Large vision-language models (VLMs) such as GPT-4 have achieved unprecedented
performance in response generation, especially with visual inputs, enabling
more creative and adaptable interaction than large language models such as
ChatGPT. Nonetheless, multimodal generation exacerbates safety concerns, since
adversaries ... | http://arxiv.org/pdf/2305.16934 | Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngai-Man Cheung, Min Lin | cs.CV, cs.CL, cs.CR, cs.LG, cs.MM | NeurIPS 2023 | null | cs.CV | 20230526 | 20231029 | 3 2 0 2
t c O 9 2 ] V C . s c [
2 v 4 3 9 6 1 . 5 0 3 2 : v i X r a
# On Evaluating Adversarial Robustness of Large Vision-Language Models
Yunqing Zhaoâ1, Tianyu Pangââ 2, Chao Duâ 2, Xiao Yang3, Chongxuan Li4, Ngai-Man Cheungâ 1, Min Lin2 1Singapore University of Technology and Design 2Sea AI Lab, Singapore 3Ts... | {
"id": "2302.13971"
} |
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