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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
2308.05481 | LLM As DBA | Database administrators (DBAs) play a crucial role in managing, maintaining
and optimizing a database system to ensure data availability, performance, and
reliability. However, it is hard and tedious for DBAs to manage a large number
of database instances (e.g., millions of instances on the cloud databases).
Recently l... | http://arxiv.org/pdf/2308.05481 | Xuanhe Zhou, Guoliang Li, Zhiyuan Liu | cs.DB, cs.AI, cs.CL, cs.LG | null | null | cs.DB | 20230810 | 20230811 | 3 2 0 2 g u A 1 1 ] B D . s c [
2 v 1 8 4 5 0 . 8 0 3 2 : v i X r a
Xuanhe Zhou Tsinghua University Beijing, China zhouxuan19@mails.tsinghua.edu.cn
# LLM As DBA Guoliang Li Tsinghua University Beijing, China liguoliang@tsinghua.edu.cn
Zhiyuan Liu Tsinghua University Beijing, China liuzy@tsinghua.edu.cn
# ABSTRACT
Datab... | {
"id": "2307.07924"
} |
2308.04030 | Gentopia: A Collaborative Platform for Tool-Augmented LLMs | Augmented Language Models (ALMs) empower large language models with the
ability to use tools, transforming them into intelligent agents for real-world
interactions. However, most existing frameworks for ALMs, to varying degrees,
are deficient in the following critical features: flexible customization,
collaborative dem... | http://arxiv.org/pdf/2308.04030 | Binfeng Xu, Xukun Liu, Hua Shen, Zeyu Han, Yuhan Li, Murong Yue, Zhiyuan Peng, Yuchen Liu, Ziyu Yao, Dongkuan Xu | cs.AI | null | null | cs.AI | 20230808 | 20230808 | 3 2 0 2
g u A 8 ] I A . s c [
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# Gentopia.AI : A Collaborative Platform for Tool-Augmented LLMs
# Binfeng Xu, Xukun Liu, Hua Shen, Zeyu Han, Yuhan Li, Murong Yue, Zhiyuan Peng, Yuchen Liu, Ziyu Yao, Dongkuan Xu https://github.com/Gentopia-AI
# Abstract
Augmented Language Models (ALMs... | {
"id": "2302.13971"
} |
2308.04026 | AgentSims: An Open-Source Sandbox for Large Language Model Evaluation | With ChatGPT-like large language models (LLM) prevailing in the community,
how to evaluate the ability of LLMs is an open question. Existing evaluation
methods suffer from following shortcomings: (1) constrained evaluation
abilities, (2) vulnerable benchmarks, (3) unobjective metrics. We suggest that
task-based evaluat... | http://arxiv.org/pdf/2308.04026 | Jiaju Lin, Haoran Zhao, Aochi Zhang, Yiting Wu, Huqiuyue Ping, Qin Chen | cs.AI, 14J60 (Primary) 14F05, 14J26 (Secondary) MSC-class: 14J60 (Primary)
14F05, 14J26 (Secondary) 68T42 | submit to EMNLP2023 demo track | null | cs.AI | 20230808 | 20230808 | 3 2 0 2
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AgentSims: An Open-Source Sandbox for Large Language Model Evaluation Jiaju Lin1,2, Haoran Zhao1,3 â, Aochi Zhang1, Yiting Wu1,4, Huqiuyue Ping1,5, Qin Chen6 1PTA Studio 2 Pennsylvania State University, 3 Beihang University, 4 Sun Yat-sen University, 5... | {
"id": "2009.03300"
} |
2308.03983 | SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI Tool | Large Language Model (LLM) based Generative AI systems have seen significant
progress in recent years. Integrating a knowledge retrieval architecture allows
for seamless integration of private data into publicly available Generative AI
systems using pre-trained LLM without requiring additional model fine-tuning.
Moreov... | http://arxiv.org/pdf/2308.03983 | Youyang Ng, Daisuke Miyashita, Yasuto Hoshi, Yasuhiro Morioka, Osamu Torii, Tomoya Kodama, Jun Deguchi | cs.CL, cs.AI | 12 pages, 6 figures | null | cs.CL | 20230808 | 20230808 | 3 2 0 2
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# SimplyRetrieve: A Private and Lightweight Retrieval-Centric Generative AI Tool
Youyang Ng, Daisuke Miyashita, Yasuto Hoshi, Yasuhiro Morioka, Osamu Torii, Tomoya Kodama, Jun Deguchi Kioxia Corporation, Japan youyang.ng@kioxia.com
# Abstract
Large Langu... | {
"id": "2302.13971"
} |
2308.03688 | AgentBench: Evaluating LLMs as Agents | Large Language Models (LLMs) are becoming increasingly smart and autonomous,
targeting real-world pragmatic missions beyond traditional NLP tasks. As a
result, there has been an urgent need to evaluate LLMs as agents on challenging
tasks in interactive environments. We present AgentBench, a multi-dimensional
evolving b... | http://arxiv.org/pdf/2308.03688 | Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, Jie Tang | cs.AI, cs.CL, cs.LG | 55 pages | null | cs.AI | 20230807 | 20231025 | 3 2 0 2
t c O 5 2 ] I A . s c [
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Technical Report (v0.2)
# AGENTBENCH: EVALUATING LLMS AS AGENTS
Xiao Liu1,*, Hao Yu1,*, Hanchen Zhang1, Yifan Xu1, Xuanyu Lei1, Hanyu Lai1, Yu Gu2, Hangliang Ding1, Kaiwen Men1, Kejuan Yang1, Shudan Zhang1, Xiang Deng2, Aohan Zeng1, Zhengxiao Du1, Chen... | {
"id": "2204.02311"
} |
2308.03656 | Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench | Evaluating Large Language Models' (LLMs) anthropomorphic capabilities has
become increasingly important in contemporary discourse. Utilizing the emotion
appraisal theory from psychology, we propose to evaluate the empathy ability of
LLMs, i.e., how their feelings change when presented with specific situations.
After a ... | http://arxiv.org/pdf/2308.03656 | Jen-tse Huang, Man Ho Lam, Eric John Li, Shujie Ren, Wenxuan Wang, Wenxiang Jiao, Zhaopeng Tu, Michael R. Lyu | cs.CL | 16 pages. Added demographic distribution of the user study. Added
ethics statements and limitations | null | cs.CL | 20230807 | 20240104 | 4 2 0 2 n a J 4 ] L C . s c [
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Emotionally Numb or Empathetic? Evaluating How LLMs Feel Using EmotionBench
EMOTIONALLY NUMB OR EMPATHETIC? EVALUATING HOW LLMS FEEL USING EMOTIONBENCH
Jen-tse Huang1,3, Man Ho Lam1, Eric John Li1, Shujie Ren2, Wenxuan Wang1,3, Wenxiang Jiao3â, Zhaopen... | {
"id": "2303.13648"
} |
2308.03427 | TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage | With recent advancements in natural language processing, Large Language
Models (LLMs) have emerged as powerful tools for various real-world
applications. Despite their prowess, the intrinsic generative abilities of LLMs
may prove insufficient for handling complex tasks which necessitate a
combination of task planning a... | http://arxiv.org/pdf/2308.03427 | Jingqing Ruan, Yihong Chen, Bin Zhang, Zhiwei Xu, Tianpeng Bao, Guoqing Du, Shiwei Shi, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao | cs.AI | Accepted in NeurIPS-2023 Workshop on Foundation Models for Decision
Making | null | cs.AI | 20230807 | 20231107 | 3 2 0 2 v o N 7 ] I A . s c [
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# TPTU: Large Language Model-based AI Agents for Task Planning and Tool Usage
Jingqing Ruanâ â¡ ruanjingqing@sensetime.com
Yihong Chenâ â¡ chenyihong@sensetime.com
# Bin Zhangâ â¡ zhangbin11@sensetime.com
# Zhiwei Xuâ â¡ xuzhiwei@sensetime.com
# ... | {
"id": "2302.13971"
} |
2308.03313 | Quantifying the Impact of Large Language Models on Collective Opinion Dynamics | The process of opinion expression and exchange is a critical component of
democratic societies. As people interact with large language models (LLMs) in
the opinion shaping process different from traditional media, the impacts of
LLMs are increasingly recognized and being concerned. However, the knowledge
about how LLMs... | http://arxiv.org/pdf/2308.03313 | Chao Li, Xing Su, Haoying Han, Cong Xue, Chunmo Zheng, Chao Fan | cs.SI, cs.CY | 21 pages, 4figures,2tables | null | cs.SI | 20230807 | 20230826 | # Quantifying the Impact of Large Language Models on Collective Opinion Dynamics
# Chao Lia, Xing Sua*, Haoying Hana, Cong Xuea, Chunmo Zhenga, Chao Fanb
a College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, Zhejiang, 310000 b College of Engineering, Computing, and Applied Sciences, Clemson Un... | {
"id": "2201.01322"
} |
2308.03210 | Time-Parameterized Convolutional Neural Networks for Irregularly Sampled Time Series | Irregularly sampled multivariate time series are ubiquitous in several
application domains, leading to sparse, not fully-observed and non-aligned
observations across different variables. Standard sequential neural network
architectures, such as recurrent neural networks (RNNs) and convolutional
neural networks (CNNs), ... | http://arxiv.org/pdf/2308.03210 | Chrysoula Kosma, Giannis Nikolentzos, Michalis Vazirgiannis | cs.LG | null | null | cs.LG | 20230806 | 20230809 | 3 2 0 2
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# Time-Parameterized Convolutional Neural Networks for Irregularly Sampled Time Series
# Chrysoula Kosma ´Ecole Polytechnique, IP Paris France kosma@lix.polytechnique.fr
# Giannis Nikolentzos ´Ecole Polytechnique, IP Paris France nikolentzos@lix.polyte... | {
"id": "1710.04110"
} |
2308.03022 | SAPIEN: Affective Virtual Agents Powered by Large Language Models | In this demo paper, we introduce SAPIEN, a platform for high-fidelity virtual
agents driven by large language models that can hold open domain conversations
with users in 13 different languages, and display emotions through facial
expressions and voice. The platform allows users to customize their virtual
agent's perso... | http://arxiv.org/pdf/2308.03022 | Masum Hasan, Cengiz Ozel, Sammy Potter, Ehsan Hoque | cs.HC, cs.AI | null | 2023 11th International Conference on Affective Computing and
Intelligent Interaction Workshops and Demos (ACIIW) | cs.HC | 20230806 | 20230806 | 3 2 0 2 g u A 6 ] C H . s c [
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2023 11th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)
# SAPIEN: Affective Virtual Agents Powered by Large Language Models*
Masum Hasanâ, Cengiz Ozelâ , Sammy Potterâ¡ and Ehsan Hoque§ Depar... | {
"id": "1703.10135"
} |
2308.02773 | EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education | EduChat (https://www.educhat.top/) is a large-scale language model
(LLM)-based chatbot system in the education domain. Its goal is to support
personalized, fair, and compassionate intelligent education, serving teachers,
students, and parents. Guided by theories from psychology and education, it
further strengthens edu... | http://arxiv.org/pdf/2308.02773 | Yuhao Dan, Zhikai Lei, Yiyang Gu, Yong Li, Jianghao Yin, Jiaju Lin, Linhao Ye, Zhiyan Tie, Yougen Zhou, Yilei Wang, Aimin Zhou, Ze Zhou, Qin Chen, Jie Zhou, Liang He, Xipeng Qiu | cs.CL | null | null | cs.CL | 20230805 | 20230805 | 3 2 0 2
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EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education Yuhao Dan1â, Zhikai Lei1â, Yiyang Gu1â, Yong Li1, Jianghao Yin1, Jiaju Lin1, Linhao Ye1, Zhiyan Tie1, Yougen Zhou1, Yilei Wang2, Aimin Zhou1,2, Ze Zhou4 Qin Chen1â... | {
"id": "2302.13971"
} |
2308.02490 | MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities | We propose MM-Vet, an evaluation benchmark that examines large multimodal
models (LMMs) on complicated multimodal tasks. Recent LMMs have shown various
intriguing abilities, such as solving math problems written on the blackboard,
reasoning about events and celebrities in news images, and explaining visual
jokes. Rapid... | http://arxiv.org/pdf/2308.02490 | Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, Lijuan Wang | cs.AI, cs.CL, cs.CV, cs.LG | Add results of GPT-4V. Code, data and leaderboard:
https://github.com/yuweihao/MM-Vet | null | cs.AI | 20230804 | 20231024 | 3 2 0 2
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# MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
# Weihao Yu1â Zhengyuan Yang2â Linjie Li2 Jianfeng Wang2 Kevin Lin2 Zicheng Liu2 Xinchao Wang1â Lijuan Wang2â
# 1National University of Singapore weihaoyu@u.nus.edu
# 2Mi... | {
"id": "2302.13971"
} |
2308.02151 | Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization | Recent months have seen the emergence of a powerful new trend in which large
language models (LLMs) are augmented to become autonomous language agents
capable of performing objective oriented multi-step tasks on their own, rather
than merely responding to queries from human users. Most existing language
agents, however... | http://arxiv.org/pdf/2308.02151 | Weiran Yao, Shelby Heinecke, Juan Carlos Niebles, Zhiwei Liu, Yihao Feng, Le Xue, Rithesh Murthy, Zeyuan Chen, Jianguo Zhang, Devansh Arpit, Ran Xu, Phil Mui, Huan Wang, Caiming Xiong, Silvio Savarese | cs.CL, cs.AI | null | null | cs.CL | 20230804 | 20230804 | 3 2 0 2
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# RETROFORMER: RETROSPECTIVE LARGE LANGUAGE AGENTS WITH POLICY GRADIENT OPTIMIZATION
# Weiran Yaoâ Shelby Heineckeâ Xueâ Rithesh Murthyâ Zeyuan Chenâ Juan Carlos Nieblesâ Zhiwei Liuâ Yihao Fengâ Le Jianguo Zhangâ Devansh Arpitâ Ran Xu... | {
"id": "2303.17580"
} |
2308.01552 | InterAct: Exploring the Potentials of ChatGPT as a Cooperative Agent | This research paper delves into the integration of OpenAI's ChatGPT into
embodied agent systems, evaluating its influence on interactive decision-making
benchmark. Drawing a parallel to the concept of people assuming roles according
to their unique strengths, we introduce InterAct. In this approach, we feed
ChatGPT wit... | http://arxiv.org/pdf/2308.01552 | Po-Lin Chen, Cheng-Shang Chang | cs.AI, cs.CL, cs.LG | null | null | cs.AI | 20230803 | 20230803 | 3 2 0 2
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# InterAct: Exploring the Potentials of ChatGPT as a Cooperative Agent
Po-Lin Chen and Cheng-Shang Chang, Fellow, IEEE
AbstractâThis research paper delves into the integration of OpenAIâs ChatGPT into embodied agent systems, evaluating its influence ... | {
"id": "2206.07682"
} |
2308.01542 | Memory Sandbox: Transparent and Interactive Memory Management for Conversational Agents | The recent advent of large language models (LLM) has resulted in
high-performing conversational agents such as chatGPT. These agents must
remember key information from an ongoing conversation to provide responses that
are contextually relevant to the user. However, these agents have limited
memory and can be distracted... | http://arxiv.org/pdf/2308.01542 | Ziheng Huang, Sebastian Gutierrez, Hemanth Kamana, Stephen MacNeil | cs.HC | null | null | cs.HC | 20230803 | 20230803 | 3 2 0 2
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# Memory Sandbox: Transparent and Interactive Memory Management for Conversational Agents
# Ziheng Huang z8huang@ucsd.edu University of CaliforniaâSan Diego San Diego, CA, USA
Sebastian Gutierrez guts@temple.edu Temple University Philadelphia, PA, USA
... | {
"id": "2210.08750"
} |
2308.01320 | DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales | ChatGPT-like models have revolutionized various applications in artificial
intelligence, from summarization and coding to translation, matching or even
surpassing human performance. However, the current landscape lacks an
accessible, efficient, and cost-effective end-to-end RLHF (Reinforcement
Learning with Human Feedb... | http://arxiv.org/pdf/2308.01320 | Zhewei Yao, Reza Yazdani Aminabadi, Olatunji Ruwase, Samyam Rajbhandari, Xiaoxia Wu, Ammar Ahmad Awan, Jeff Rasley, Minjia Zhang, Conglong Li, Connor Holmes, Zhongzhu Zhou, Michael Wyatt, Molly Smith, Lev Kurilenko, Heyang Qin, Masahiro Tanaka, Shuai Che, Shuaiwen Leon Song, Yuxiong He | cs.LG, cs.AI, cs.CL | 14 pages, 7 figures | null | cs.LG | 20230802 | 20230802 | 3 2 0 2
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DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales
Zhewei Yao, Reza Yazdani Aminabadi, Olatunji Ruwase, Samyam Rajbhandari, Xiaoxia Wu, Ammar Ahmad Awan, Jeff Rasley, Minjia Zhang, Conglong Li, Connor Holmes, Zho... | {
"id": "1707.06347"
} |
2308.01390 | OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models | We introduce OpenFlamingo, a family of autoregressive vision-language models
ranging from 3B to 9B parameters. OpenFlamingo is an ongoing effort to produce
an open-source replication of DeepMind's Flamingo models. On seven
vision-language datasets, OpenFlamingo models average between 80 - 89% of
corresponding Flamingo ... | http://arxiv.org/pdf/2308.01390 | Anas Awadalla, Irena Gao, Josh Gardner, Jack Hessel, Yusuf Hanafy, Wanrong Zhu, Kalyani Marathe, Yonatan Bitton, Samir Gadre, Shiori Sagawa, Jenia Jitsev, Simon Kornblith, Pang Wei Koh, Gabriel Ilharco, Mitchell Wortsman, Ludwig Schmidt | cs.CV, cs.AI, cs.LG | null | null | cs.CV | 20230802 | 20230807 | 3 2 0 2
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OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
Anas Awadallaâ1 Irena Gaoâ2 Josh Gardner1 Jack Hessel3 Yusuf Hanafy1 Wanrong Zhu5 Shiori Sagawa2 Kalyani Marathe1 Jenia Jitsev4,9 Yonatan Bitton6 Simon Ko... | {
"id": "1909.11059"
} |
2308.01423 | ChatMOF: An Autonomous AI System for Predicting and Generating Metal-Organic Frameworks | ChatMOF is an autonomous Artificial Intelligence (AI) system that is built to
predict and generate metal-organic frameworks (MOFs). By leveraging a
large-scale language model (GPT-4 and GPT-3.5-turbo), ChatMOF extracts key
details from textual inputs and delivers appropriate responses, thus
eliminating the necessity fo... | http://arxiv.org/pdf/2308.01423 | Yeonghun Kang, Jihan Kim | cs.CL, cs.AI, cs.LG, physics.chem-ph | null | null | cs.CL | 20230801 | 20230825 | # ChatMOF: An Autonomous AI System for
# Predicting and Generating Metal-Organic
# Frameworks
Yeonghun Kang, Jihan Kim*
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
1
# ABSTRACT
ChatMOF is an... | {
"id": "2302.13971"
} |
2308.00675 | Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models | Today, large language models (LLMs) are taught to use new tools by providing
a few demonstrations of the tool's usage. Unfortunately, demonstrations are
hard to acquire, and can result in undesirable biased usage if the wrong
demonstration is chosen. Even in the rare scenario that demonstrations are
readily available, ... | http://arxiv.org/pdf/2308.00675 | Cheng-Yu Hsieh, Si-An Chen, Chun-Liang Li, Yasuhisa Fujii, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister | cs.CL, cs.AI, cs.CV, cs.LG | null | null | cs.CL | 20230801 | 20230801 | 3 2 0 2
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# Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models
Cheng-Yu Hsieh1â , Si-An Chen2â , Chun-Liang Li3, Yasuhisa Fujii4, Alexander Ratner1, Chen-Yu Lee3, Ranjay Krishna1â, Tomas Pfister3â 1University of Washington, 2National ... | {
"id": "2302.13971"
} |
2308.00436 | SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning | The recent progress in large language models (LLMs), especially the invention
of chain-of-thought prompting, has made it possible to automatically answer
questions by stepwise reasoning. However, when faced with more complicated
problems that require non-linear thinking, even the strongest LLMs make
mistakes. To addres... | http://arxiv.org/pdf/2308.00436 | Ning Miao, Yee Whye Teh, Tom Rainforth | cs.AI, cs.CL, cs.LG | null | null | cs.AI | 20230801 | 20231005 | 3 2 0 2
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# SELFCHECK: USING LLMS TO ZERO-SHOT CHECK THEIR OWN STEP-BY-STEP REASONING
# Ning Miao1* Yee Whye Teh1 Tom Rainforth1
ABSTRACT The recent progress in large language models (LLMs), especially the invention of chain-of-thought prompting, has made it possi... | {
"id": "2206.02336"
} |
2308.00352 | MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework | Remarkable progress has been made on automated problem solving through
societies of agents based on large language models (LLMs). Existing LLM-based
multi-agent systems can already solve simple dialogue tasks. Solutions to more
complex tasks, however, are complicated through logic inconsistencies due to
cascading hallu... | http://arxiv.org/pdf/2308.00352 | Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, Jürgen Schmidhuber | cs.AI, cs.MA | null | null | cs.AI | 20230801 | 20231106 | 3 2 0 2
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Preprint
# METAGPT: META PROGRAMMING FOR A MULTI-AGENT COLLABORATIVE FRAMEWORK
Sirui Hong1â, Mingchen Zhuge2â, Jonathan Chen1, Xiawu Zheng3, Yuheng Cheng4, Ceyao Zhang4, Jinlin Wang1, Zili Wang, Steven Ka Shing Yau5, Zijuan Lin4, Liyang Zhou6, Chenyu... | {
"id": "2308.12950"
} |
2308.00245 | The Hitchhiker's Guide to Program Analysis: A Journey with Large Language Models | Static analysis is a widely used technique in software engineering for
identifying and mitigating bugs. However, a significant hurdle lies in
achieving a delicate balance between precision and scalability. Large Language
Models (LLMs) offer a promising alternative, as recent advances demonstrate
remarkable capabilities... | http://arxiv.org/pdf/2308.00245 | Haonan Li, Yu Hao, Yizhuo Zhai, Zhiyun Qian | cs.SE, cs.AI | null | null | cs.SE | 20230801 | 20231115 | 3 2 0 2
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# The Hitchhikerâs Guide to Program Analysis: A Journey with Large Language Models
Haonan Li hli333@ucr.edu UC Riverside Riverside, California, USA
Yu Hao yhao016@ucr.edu UC Riverside Riverside, California, USA
Yizhuo Zhai yzhai003@ucr.edu UC Riversi... | {
"id": "2305.10601"
} |
2307.16789 | ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs | Despite the advancements of open-source large language models (LLMs), e.g.,
LLaMA, they remain significantly limited in tool-use capabilities, i.e., using
external tools (APIs) to fulfill human instructions. The reason is that current
instruction tuning largely focuses on basic language tasks but ignores the
tool-use d... | http://arxiv.org/pdf/2307.16789 | Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Lauren Hong, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, Dahai Li, Zhiyuan Liu, Maosong Sun | cs.AI, cs.CL, cs.LG | null | null | cs.AI | 20230731 | 20231003 | 3 2 0 2
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Preprint
u/s @
TOOLLLM: FACILITATING LARGE LANGUAGE MODELS TO MASTER 16000+ REAL-WORLD APIS
Yujia Qin1â, Shihao Liang1â, Yining Ye1, Kunlun Zhu1, Lan Yan1, Yaxi Lu1, Yankai Lin3â , Xin Cong1, Xiangru Tang4, Bill Qian4, Sihan Zhao1, Lauren Hong1, Ru... | {
"id": "2302.13971"
} |
2307.16877 | Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering | Retriever-augmented instruction-following models are attractive alternatives
to fine-tuned approaches for information-seeking tasks such as question
answering (QA). By simply prepending retrieved documents in its input along
with an instruction, these models can be adapted to various information domains
and tasks witho... | http://arxiv.org/pdf/2307.16877 | Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, Nicholas Meade, Siva Reddy | cs.CL, cs.AI | null | null | cs.CL | 20230731 | 20230731 | 3 2 0 2
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# Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering
# Vaibhav Adlakha1,2 Parishad BehnamGhader1,2,â Xing Han Lu1,2,â Nicholas Meade1,2,â Siva Reddy1,2,3
1Mila â Quebec AI Institute 2McGill University... | {
"id": "2201.08239"
} |
2307.16364 | Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generators | With their remarkable ability to generate code, large language models (LLMs)
are a transformative technology for computing education practice. They have
created an urgent need for educators to rethink pedagogical approaches and
teaching strategies for newly emerging skill sets. Traditional approaches to
learning progra... | http://arxiv.org/pdf/2307.16364 | Paul Denny, Juho Leinonen, James Prather, Andrew Luxton-Reilly, Thezyrie Amarouche, Brett A. Becker, Brent N. Reeves | cs.HC, cs.AI | null | null | cs.HC | 20230731 | 20230731 | # Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generators
Paul Denny The University of Auckland Auckland, New Zealand paul@cs.auckland.ac.nz
Juho Leinonen The University of Auckland Auckland, New Zealand juho.leinonen@auckland.ac.nz
James Prather Abilene Christian University Abil... | {
"id": "2306.04556"
} |
2307.16125 | SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension | Based on powerful Large Language Models (LLMs), recent generative Multimodal
Large Language Models (MLLMs) have gained prominence as a pivotal research
area, exhibiting remarkable capability for both comprehension and generation.
In this work, we address the evaluation of generative comprehension in MLLMs as
a prelimin... | http://arxiv.org/pdf/2307.16125 | Bohao Li, Rui Wang, Guangzhi Wang, Yuying Ge, Yixiao Ge, Ying Shan | cs.CL, cs.CV | Technical Report; Project released at:
https://github.com/AILab-CVC/SEED-Bench | null | cs.CL | 20230730 | 20230802 | 3 2 0 2
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# SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Bohao Li1â Rui Wang1â Guangzhi Wang2â Yuying Ge1â Yixiao Ge1,2â Ying Shan1,2
1Tencent AI Lab 2ARC Lab, Tencent PCG
https://github.com/AILab-CVC/SEED-Bench
# Abstract
Based... | {
"id": "2306.05424"
} |
2307.15833 | Dialogue Shaping: Empowering Agents through NPC Interaction | One major challenge in reinforcement learning (RL) is the large amount of
steps for the RL agent needs to converge in the training process and learn the
optimal policy, especially in text-based game environments where the action
space is extensive. However, non-player characters (NPCs) sometimes hold some
key informati... | http://arxiv.org/pdf/2307.15833 | Wei Zhou, Xiangyu Peng, Mark Riedl | cs.CL | null | null | cs.CL | 20230728 | 20230728 | 3 2 0 2
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# Dialogue Shaping: Empowering Agents through NPC Interaction
# Wei Zhou, Xiangyu Peng and Mark Riedl
Georgia Institute of Technology, Atlanta, GA, 30332, USA
Abstract One major challenge in reinforcement learning (RL) is the large amount of steps for ... | {
"id": "2301.10107"
} |
2307.15818 | RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control | We study how vision-language models trained on Internet-scale data can be
incorporated directly into end-to-end robotic control to boost generalization
and enable emergent semantic reasoning. Our goal is to enable a single
end-to-end trained model to both learn to map robot observations to actions and
enjoy the benefit... | http://arxiv.org/pdf/2307.15818 | Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, Pete Florence, Chuyuan Fu, Montse Gonzalez Arenas, Keerthana Gopalakrishnan, Kehang Han, Karol Hausman, Alexander Herzog, Jasmine Hsu, Brian Ichter, Alex Irpan, Nikhil J... | cs.RO, cs.CL, cs.CV, cs.LG | Website: https://robotics-transformer.github.io/ | null | cs.RO | 20230728 | 20230728 | 3 2 0 2
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âo) Google DeepMind
https://robotics-transformer2.github.io 2023-8-1
2023-8-1
# RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromansk... | {
"id": "2304.02643"
} |
2307.15337 | Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding | This work aims at decreasing the end-to-end generation latency of large
language models (LLMs). One of the major causes of the high generation latency
is the sequential decoding approach adopted by almost all state-of-the-art
LLMs. In this work, motivated by the thinking and writing process of humans, we
propose Skelet... | http://arxiv.org/pdf/2307.15337 | Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang, Huazhong Yang, Yu Wang | cs.CL, cs.AI | Technical report | null | cs.CL | 20230728 | 20231008 | 3 2 0 2
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Skeleton-of-Thought: Large Language Models Can Do Parallel Decoding
SKELETON-OF-THOUGHT: LARGE LANGUAGE MOD- ELS CAN DO PARALLEL DECODING
Xuefei Ning1â foxdoraame@gmail.com
Zinan Lin2â linzinan1995@gmail.com
# Zixuan Zhou1â zhouzx21@mails.tsinghua.... | {
"id": "2302.13971"
} |
2307.15217 | Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback | Reinforcement learning from human feedback (RLHF) is a technique for training
AI systems to align with human goals. RLHF has emerged as the central method
used to finetune state-of-the-art large language models (LLMs). Despite this
popularity, there has been relatively little public work systematizing its
flaws. In thi... | http://arxiv.org/pdf/2307.15217 | Stephen Casper, Xander Davies, Claudia Shi, Thomas Krendl Gilbert, Jérémy Scheurer, Javier Rando, Rachel Freedman, Tomasz Korbak, David Lindner, Pedro Freire, Tony Wang, Samuel Marks, Charbel-Raphaël Segerie, Micah Carroll, Andi Peng, Phillip Christoffersen, Mehul Damani, Stewart Slocum, Usman Anwar, Anand Siththaranja... | cs.AI, cs.CL, cs.LG | null | null | cs.AI | 20230727 | 20230911 | 3 2 0 2
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# Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Stephen Casper,â MIT CSAIL, Xander Davies,â Harvard University scasper@mit.edu
Claudia Shi, Columbia University Thomas Krendl Gilbert, Cornell Tech Jérémy S... | {
"id": "2305.20050"
} |
2307.14984 | S3: Social-network Simulation System with Large Language Model-Empowered Agents | Social network simulation plays a crucial role in addressing various
challenges within social science. It offers extensive applications such as
state prediction, phenomena explanation, and policy-making support, among
others. In this work, we harness the formidable human-like capabilities
exhibited by large language mo... | http://arxiv.org/pdf/2307.14984 | Chen Gao, Xiaochong Lan, Zhihong Lu, Jinzhu Mao, Jinghua Piao, Huandong Wang, Depeng Jin, Yong Li | cs.SI | null | null | cs.SI | 20230727 | 20231019 | 3 2 0 2
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# S3: Social-network Simulation System with Large Language Model-Empowered Agents
Chen Gao, Xiaochong Lan, Zhihong Lu, Jinzhu Mao, Jinghua Piao, Huandong Wang, Depeng Jin, Yong Li Department of Electronic Engineering, Tsinghua University liyong07@tsing... | {
"id": "2302.13971"
} |
2307.14430 | Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models | The quality of training data impacts the performance of pre-trained large
language models (LMs). Given a fixed budget of tokens, we study how to best
select data that leads to good downstream model performance across tasks. We
develop a new framework based on a simple hypothesis: just as humans acquire
interdependent s... | http://arxiv.org/pdf/2307.14430 | Mayee F. Chen, Nicholas Roberts, Kush Bhatia, Jue Wang, Ce Zhang, Frederic Sala, Christopher Ré | cs.CL, cs.LG | null | null | cs.CL | 20230726 | 20230726 | 3 2 0 2
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# Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models
# Mayee F. Chen*1 Nicholas Roberts2 Kush Bhatia1 Jue Wang3 Ce Zhang3, 4 Frederic Sala2 Christopher Ré1
1Department of Computer Science, Stanford University 2Depa... | {
"id": "2101.00027"
} |
2307.14225 | Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences | Traditional recommender systems leverage users' item preference history to
recommend novel content that users may like. However, modern dialog interfaces
that allow users to express language-based preferences offer a fundamentally
different modality for preference input. Inspired by recent successes of
prompting paradi... | http://arxiv.org/pdf/2307.14225 | Scott Sanner, Krisztian Balog, Filip Radlinski, Ben Wedin, Lucas Dixon | cs.IR, cs.LG | To appear at RecSys'23 | null | cs.IR | 20230726 | 20230726 | # ee
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# Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences
SCOTT SANNERâ, University of Toronto, Canada KRISZTIAN BALOG, Google, Norway FILIP RADLINSKI, Google, United Kingdom BEN WEDIN, Google,... | {
"id": "2305.06474"
} |
2307.13779 | Is GPT a Computational Model of Emotion? Detailed Analysis | This paper investigates the emotional reasoning abilities of the GPT family
of large language models via a component perspective. The paper first examines
how the model reasons about autobiographical memories. Second, it
systematically varies aspects of situations to impact emotion intensity and
coping tendencies. Even... | http://arxiv.org/pdf/2307.13779 | Ala N. Tak, Jonathan Gratch | cs.CL, cs.AI, cs.CY, cs.HC | null | null | cs.CL | 20230725 | 20230725 | # Is GPT a Computational Model of Emotion? Detailed Analysis
Ala N. Tak and Jonathan Gratch Institute for Creative Technologies University of Southern California Playa Vista, CA 90094, USA. nekouvag@usc.edu, gratch@ict.usc.edu
# Contents
1.1 Original prompts ................................................................ | {
"id": "2302.08399"
} |
2307.13528 | FacTool: Factuality Detection in Generative AI -- A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios | The emergence of generative pre-trained models has facilitated the synthesis
of high-quality text, but it has also posed challenges in identifying factual
errors in the generated text. In particular: (1) A wider range of tasks now
face an increasing risk of containing factual errors when handled by generative
models. (... | http://arxiv.org/pdf/2307.13528 | I-Chun Chern, Steffi Chern, Shiqi Chen, Weizhe Yuan, Kehua Feng, Chunting Zhou, Junxian He, Graham Neubig, Pengfei Liu | cs.CL, cs.AI | null | null | cs.CL | 20230725 | 20230726 | Pengfei Liu1,7â
FACTOOL: Factuality Detection in Generative AI A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios Shiqi Chen3 Weizhe Yuan4 Kehua Feng1
# I-Chun Chern2 Stefï¬ Chern2 Pengfei Liu1,7â Chunting Zhou5 Junxian He6 Graham Neubig2
1Shanghai Jiao Tong University 2Carnegie Mellon Universit... | {
"id": "2110.14168"
} |
2308.02439 | A large language model-assisted education tool to provide feedback on open-ended responses | Open-ended questions are a favored tool among instructors for assessing
student understanding and encouraging critical exploration of course material.
Providing feedback for such responses is a time-consuming task that can lead to
overwhelmed instructors and decreased feedback quality. Many instructors resort
to simple... | http://arxiv.org/pdf/2308.02439 | Jordan K. Matelsky, Felipe Parodi, Tony Liu, Richard D. Lange, Konrad P. Kording | cs.CY, cs.AI | null | null | cs.CY | 20230725 | 20230725 | 3 2 0 2
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# A large language model-assisted education tool to provide feedback on open-ended responses
# Jordan K. Matelsky Richard D. Lange 1,2, Felipe Parodi 3, Tony Liu 4, 1,5, and Konrad P. Kording 1,3,4,6
1Department of Bioengineering, University of Pennsyl... | {
"id": "2106.01399"
} |
2307.13692 | ARB: Advanced Reasoning Benchmark for Large Language Models | Large Language Models (LLMs) have demonstrated remarkable performance on
various quantitative reasoning and knowledge benchmarks. However, many of these
benchmarks are losing utility as LLMs get increasingly high scores, despite not
yet reaching expert performance in these domains. We introduce ARB, a novel
benchmark c... | http://arxiv.org/pdf/2307.13692 | Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, Pranav Tadepalli, Paula Vidas, Alexander Kranias, John J. Nay, Kshitij Gupta, Aran Komatsuzaki | cs.CL, cs.LG | Submitted to NeurIPS Datasets and Benchmarks Track | null | cs.CL | 20230725 | 20230728 | 3 2 0 2
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# ARB: Advanced Reasoning Benchmark for Large Language Models
# Tomohiro Sawada1,2,â, Daniel Paleka1,3, Alexander Havrilla1,2, Pranav Tadepalli1,2, Paula Vidas1,
Alexander Kranias1,2, John J. Nay4,5, Kshitij Gupta1,6, Aran Komatsuzaki1,2,â¡â¡
1 Duc... | {
"id": "2212.14402"
} |
2307.13854 | WebArena: A Realistic Web Environment for Building Autonomous Agents | With advances in generative AI, there is now potential for autonomous agents
to manage daily tasks via natural language commands. However, current agents
are primarily created and tested in simplified synthetic environments, leading
to a disconnect with real-world scenarios. In this paper, we build an
environment for l... | http://arxiv.org/pdf/2307.13854 | Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Tianyue Ou, Yonatan Bisk, Daniel Fried, Uri Alon, Graham Neubig | cs.AI, cs.CL, cs.LG | Our code, data, environment reproduction resources, and video
demonstrations are publicly available at https://webarena.dev/ | null | cs.AI | 20230725 | 20231025 | 3 2 0 2
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Under review
WE BAR E N A: A REALISTIC WEB ENVIRONMENT FOR BUILDING AUTONOMOUS AGENTS
Shuyan Zhouâ Frank F. Xuâ Hao Zhuâ Xuhui Zhouâ Robert Loâ Abishek Sridharâ Xianyi Cheng Tianyue Ou Yonatan Bisk Daniel Fried Uri Alon Graham Neubig
# Carn... | {
"id": "2112.09332"
} |
2307.12966 | Aligning Large Language Models with Human: A Survey | Large Language Models (LLMs) trained on extensive textual corpora have
emerged as leading solutions for a broad array of Natural Language Processing
(NLP) tasks. Despite their notable performance, these models are prone to
certain limitations such as misunderstanding human instructions, generating
potentially biased co... | http://arxiv.org/pdf/2307.12966 | Yufei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, Qun Liu | cs.CL | work in progress | null | cs.CL | 20230724 | 20230724 | 3 2 0 2
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# Aligning Large Language Models with Human: A Survey
Yufei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang Lifeng Shang, Xin Jiang, Qun Liu Huawei Noahâs Ark Lab {wangyufei44,zhongwanjun1,liliangyou,mifei2,zeng.xingshan,wenyong... | {
"id": "2307.03109"
} |
2307.12950 | RLCD: Reinforcement Learning from Contrast Distillation for Language Model Alignment | We propose Reinforcement Learning from Contrast Distillation (RLCD), a method
for aligning language models to follow natural language principles without
using human feedback. RLCD trains a preference model using simulated preference
pairs that contain both a high-quality and low-quality example, generated using
contras... | http://arxiv.org/pdf/2307.12950 | Kevin Yang, Dan Klein, Asli Celikyilmaz, Nanyun Peng, Yuandong Tian | cs.CL, cs.AI | null | null | cs.CL | 20230724 | 20230818 | 3 2 0 2
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# RLCD: REINFORCEMENT LEARNING FROM CONTRAST DISTILLATION FOR LANGUAGE MODEL ALIGNMENT
Kevin Yang1,2 Dan Klein2 Asli Celikyilmaz1 Nanyun Peng3 Yuandong Tian1 1Meta AI, 2UC Berkeley, 3UCLA {yangk,klein}@berkeley.edu,{aslic,yuandong}@meta.com,violetpeng@... | {
"id": "2302.13971"
} |
2307.12856 | A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis | Pre-trained large language models (LLMs) have recently achieved better
generalization and sample efficiency in autonomous web automation. However, the
performance on real-world websites has still suffered from (1) open domainness,
(2) limited context length, and (3) lack of inductive bias on HTML. We
introduce WebAgent... | http://arxiv.org/pdf/2307.12856 | Izzeddin Gur, Hiroki Furuta, Austin Huang, Mustafa Safdari, Yutaka Matsuo, Douglas Eck, Aleksandra Faust | cs.LG, cs.AI, cs.CL | null | null | cs.LG | 20230724 | 20231003 | 3 2 0 2
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Preprint
# A REAL-WORLD WEBAGENT WITH PLANNING, LONG CONTEXT UNDERSTANDING, AND PROGRAM SYNTHESIS
Izzeddin Gur1â Hiroki Furuta1,2ââ Austin Huang1 Mustafa Safdari1 Yutaka Matsuo2 Douglas Eck1 Aleksandra Faust1 1Google DeepMind, 2The University of To... | {
"id": "2101.02235"
} |
2307.12573 | Tachikuma: Understading Complex Interactions with Multi-Character and Novel Objects by Large Language Models | Recent advancements in natural language and Large Language Models (LLMs) have
enabled AI agents to simulate human-like interactions within virtual worlds.
However, these interactions still face limitations in complexity and
flexibility, particularly in scenarios involving multiple characters and novel
objects. Pre-defi... | http://arxiv.org/pdf/2307.12573 | Yuanzhi Liang, Linchao Zhu, Yi Yang | cs.CL | Preliminary version of an ongoing work | null | cs.CL | 20230724 | 20230724 | 3 2 0 2
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# Tachikuma: Understading Complex Interactions with Multi-Character and Novel Objects by Large Language Models
Yuanzhi Liang 1, Linchao Zhu 2, Yi Yang 2 1 University of Technology Sydney, 2 Zhejiang University yuanzhi.Liang@student.uts.edu.au zhulincha... | {
"id": "2212.10060"
} |
2308.03762 | GPT-4 Can't Reason | GPT-4 was released in March 2023 to wide acclaim, marking a very substantial
improvement across the board over GPT-3.5 (OpenAI's previously best model,
which had powered the initial release of ChatGPT). However, despite the
genuinely impressive improvement, there are good reasons to be highly skeptical
of GPT-4's abili... | http://arxiv.org/pdf/2308.03762 | Konstantine Arkoudas | cs.CL | null | null | cs.CL | 20230721 | 20230810 | arXiv:2308.03762v2 2023
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# GPT-4 Canât Reason
(Position Paper)
Konstantine Arkoudas
Dyania Health
August 11, 2023
# Abstract
GPT-4 was released in March 2023 to wide acclaim, marking a very substantial improvement across the board over GPT-3.5 (OpenAI... | {
"id": "2308.03762"
} |
2307.10635 | SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models | Recent advances in large language models (LLMs) have demonstrated notable
progress on many mathematical benchmarks. However, most of these benchmarks
only feature problems grounded in junior and senior high school subjects,
contain only multiple-choice questions, and are confined to a limited scope of
elementary arithm... | http://arxiv.org/pdf/2307.10635 | Xiaoxuan Wang, Ziniu Hu, Pan Lu, Yanqiao Zhu, Jieyu Zhang, Satyen Subramaniam, Arjun R. Loomba, Shichang Zhang, Yizhou Sun, Wei Wang | cs.CL, cs.AI, cs.LG | Work in progress, 18 pages | null | cs.CL | 20230720 | 20230720 | 3 2 0 2
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# SCIBENCH: Evaluating College-Level Scientiï¬c Problem-Solving Abilities of Large Language Models
# Xiaoxuan Wangâ â Ziniu Huâ â Pan Luâ â Yanqiao Zhuâ â Jieyu Zhangâ¡ Satyen Subramaniamâ Arjun R. Loombaâ Shichang Zhangâ Yizhou Sun... | {
"id": "2302.13971"
} |
2307.11019 | Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation | Knowledge-intensive tasks (e.g., open-domain question answering (QA)) require
a substantial amount of factual knowledge and often rely on external
information for assistance. Recently, large language models (LLMs) (e.g.,
ChatGPT), have demonstrated impressive prowess in solving a wide range of tasks
with world knowledg... | http://arxiv.org/pdf/2307.11019 | Ruiyang Ren, Yuhao Wang, Yingqi Qu, Wayne Xin Zhao, Jing Liu, Hao Tian, Hua Wu, Ji-Rong Wen, Haifeng Wang | cs.CL, cs.IR | null | null | cs.CL | 20230720 | 20230723 | 3 2 0 2
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Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation Ruiyang Ren1,3â Yuhao Wang1,3 Yingqi Qu2 Wayne Xin Zhao1,3â Jing Liu2â
# Hao Tian2 Hua Wu2 Ji-Rong Wen1,3 Haifeng Wang2
1Gaoling School of Artiï¬ci... | {
"id": "2302.06476"
} |
2307.10337 | Are you in a Masquerade? Exploring the Behavior and Impact of Large Language Model Driven Social Bots in Online Social Networks | As the capabilities of Large Language Models (LLMs) emerge, they not only
assist in accomplishing traditional tasks within more efficient paradigms but
also stimulate the evolution of social bots. Researchers have begun exploring
the implementation of LLMs as the driving core of social bots, enabling more
efficient and... | http://arxiv.org/pdf/2307.10337 | Siyu Li, Jin Yang, Kui Zhao | cs.SI | 18 pages, 7 figures | null | cs.SI | 20230719 | 20230719 | 3 2 0 2
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Are you in a Masquerade? Exploring the Behavior and Impact of Large Language Model Driven Social Bots in Online Social Networks SIYU LI, Sichuan University, China JIN YANGâ, Sichuan University, China KUI ZHAO, Sichuan University, China
As the capabil... | {
"id": "2107.03374"
} |
2307.09705 | CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility | With the rapid evolution of large language models (LLMs), there is a growing
concern that they may pose risks or have negative social impacts. Therefore,
evaluation of human values alignment is becoming increasingly important.
Previous work mainly focuses on assessing the performance of LLMs on certain
knowledge and re... | http://arxiv.org/pdf/2307.09705 | Guohai Xu, Jiayi Liu, Ming Yan, Haotian Xu, Jinghui Si, Zhuoran Zhou, Peng Yi, Xing Gao, Jitao Sang, Rong Zhang, Ji Zhang, Chao Peng, Fei Huang, Jingren Zhou | cs.CL | Working in Process | null | cs.CL | 20230719 | 20230719 | 3 2 0 2
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# CVALUES: Measuring the Values of Chinese Large Language Models from Safety to Responsibility
Guohai Xu1, Jiayi Liu1, Ming Yan1â, Haotian Xu1, Jinghui Si1, Zhuoran Zhou1 Peng Yi1, Xing Gao1, Jitao Sang2, Rong Zhang1, Ji Zhang1 Chao Peng1, Fei Huang1... | {
"id": "1606.05250"
} |
2307.09288 | Llama 2: Open Foundation and Fine-Tuned Chat Models | In this work, we develop and release Llama 2, a collection of pretrained and
fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70
billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for
dialogue use cases. Our models outperform open-source chat models on most
benchmarks ... | http://arxiv.org/pdf/2307.09288 | Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanu... | cs.CL, cs.AI | null | null | cs.CL | 20230718 | 20230719 | 3 2 0 2
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# Llama 2: Open Foundation and Fine-Tuned Chat Models
Hugo Touvronâ Louis Martinâ Kevin Stoneâ Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale Dan Bikel Lukas Blecher Cristian Canton Fer... | {
"id": "2006.03654"
} |
2307.09042 | Emotional Intelligence of Large Language Models | Large Language Models (LLMs) have demonstrated remarkable abilities across
numerous disciplines, primarily assessed through tasks in language generation,
knowledge utilization, and complex reasoning. However, their alignment with
human emotions and values, which is critical for real-world applications, has
not been sys... | http://arxiv.org/pdf/2307.09042 | Xuena Wang, Xueting Li, Zi Yin, Yue Wu, Liu Jia | cs.AI | 36 pages, 5 figures | null | cs.AI | 20230718 | 20230728 | # Emotional Intelligence of Large Language Models
Xuena Wang1, Xueting Li2, Zi Yin1, Yue Wu1, & Liu Jia1*
1 Department of Psychology & Tsinghua Laboratory of Brain and Intelligence, Tsinghua University 2 Department of Psychology, Renmin University of China *Correspondence to: liujiathu@tsinghua.edu.cn (J. Liu)
# Abstra... | {
"id": "2302.02083"
} |
2307.08701 | AlpaGasus: Training A Better Alpaca with Fewer Data | Large language models~(LLMs) strengthen instruction-following capability
through instruction-finetuning (IFT) on supervised instruction/response data.
However, widely used IFT datasets (e.g., Alpaca's 52k data) surprisingly
contain many low-quality instances with incorrect or irrelevant responses,
which are misleading ... | http://arxiv.org/pdf/2307.08701 | Lichang Chen, Shiyang Li, Jun Yan, Hai Wang, Kalpa Gunaratna, Vikas Yadav, Zheng Tang, Vijay Srinivasan, Tianyi Zhou, Heng Huang, Hongxia Jin | cs.CL | 32 Pages; 29 Figures; 15 Tables | null | cs.CL | 20230717 | 20231104 | 3 2 0 2 v o N 4 ] L C . s c [
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Preprint
# ALPAGASUS: TRAINING A BETTER ALPACA WITH FEWER DATA
Lichang Chenââ , Shiyang Li ââ¡, Jun Yanâ¯, Hai Wang â¡, Kalpa Gunaratnaâ¡, Vikas Yadavâ¡, Zheng Tangâ¡, Vijay Srinivasanâ¡, Tianyi Zhouâ , Heng Huangâ , Hongxia Jinâ¡ â Univ... | {
"id": "2302.13971"
} |
2307.08691 | FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning | Scaling Transformers to longer sequence lengths has been a major problem in
the last several years, promising to improve performance in language modeling
and high-resolution image understanding, as well as to unlock new applications
in code, audio, and video generation. The attention layer is the main
bottleneck in sca... | http://arxiv.org/pdf/2307.08691 | Tri Dao | cs.LG | null | null | cs.LG | 20230717 | 20230717 | 3 2 0 2
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# FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
Tri Dao1,2
1Department of Computer Science, Princeton University 2Department of Computer Science, Stanford University trid@cs.stanford.edu
July 18, 2023
# Abstract
Scali... | {
"id": "2004.05150"
} |
2307.08674 | TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT | Tables are prevalent in real-world databases, requiring significant time and
effort for humans to analyze and manipulate. The advancements in large language
models (LLMs) have made it possible to interact with tables using natural
language input, bringing this capability closer to reality. In this paper, we
present Tab... | http://arxiv.org/pdf/2307.08674 | Liangyu Zha, Junlin Zhou, Liyao Li, Rui Wang, Qingyi Huang, Saisai Yang, Jing Yuan, Changbao Su, Xiang Li, Aofeng Su, Tao Zhang, Chen Zhou, Kaizhe Shou, Miao Wang, Wufang Zhu, Guoshan Lu, Chao Ye, Yali Ye, Wentao Ye, Yiming Zhang, Xinglong Deng, Jie Xu, Haobo Wang, Gang Chen, Junbo Zhao | cs.AI, cs.LG | Technical Report | null | cs.AI | 20230717 | 20230807 | 3 2 0 2
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# TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
Liangyu Zha1,2 Junlin Zhou1,2 Liyao Li1,2 Rui Wang1,2 Qingyi Huang3 Jing Yuan3 Changbao Su3 Xiang Li3 Aofeng Su3 Tao Zhang3 Saisai Yang3 Chen Zhou3 Kaizhe Shou Miao Wang Wufan... | {
"id": "2302.13971"
} |
2307.08621 | Retentive Network: A Successor to Transformer for Large Language Models | In this work, we propose Retentive Network (RetNet) as a foundation
architecture for large language models, simultaneously achieving training
parallelism, low-cost inference, and good performance. We theoretically derive
the connection between recurrence and attention. Then we propose the retention
mechanism for sequen... | http://arxiv.org/pdf/2307.08621 | Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, Furu Wei | cs.CL, cs.LG | null | null | cs.CL | 20230717 | 20230809 | 3 2 0 2
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# Retentive Network: A Successor to Transformer for Large Language Models
# Yutao Sunâ â â¡ Li Dongâ â Shaohan Huangâ Shuming Maâ Yuqing Xiaâ Jilong Xueâ Jianyong Wangâ¡ Furu Weiâ â
# â Microsoft Research
# â¡ Tsinghua University
#... | {
"id": "2104.02112"
} |
2307.08303 | Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models | Dense retrieval (DR) converts queries and documents into dense embeddings and
measures the similarity between queries and documents in vector space. One of
the challenges in DR is the lack of domain-specific training data. While DR
models can learn from large-scale public datasets like MS MARCO through
transfer learnin... | http://arxiv.org/pdf/2307.08303 | Zhiyuan Peng, Xuyang Wu, Yi Fang | cs.IR, cs.AI, cs.CL, cs.LG | fix typos | null | cs.IR | 20230717 | 20230829 | 3 2 0 2
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# Soft Prompt Tuning for Augmenting Dense Retrieval with Large Language Models
Zhiyuan Pengâ Santa Clara University Santa Clara, USA zpeng@scu.edu
Xuyang Wuâ Santa Clara University Santa Clara, USA xwu5@scu.edu
Yi Fang Santa Clara University Santa ... | {
"id": "2302.13971"
} |
2307.08074 | Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling | Modeling discourse -- the linguistic phenomena that go beyond individual
sentences, is a fundamental yet challenging aspect of natural language
processing (NLP). However, existing evaluation benchmarks primarily focus on
the evaluation of inter-sentence properties and overlook critical discourse
phenomena that cross se... | http://arxiv.org/pdf/2307.08074 | Longyue Wang, Zefeng Du, Donghuai Liu, Deng Cai, Dian Yu, Haiyun Jiang, Yan Wang, Leyang Cui, Shuming Shi, Zhaopeng Tu | cs.CL, cs.AI | Zhaopeng Tu is the corresponding author | null | cs.CL | 20230716 | 20230722 | 3 2 0 2
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# Preprint
# Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling
Longyue Wang, Zefeng Du, Donghuai Liu, Cai Deng, Dian Yu, Haiyun Jiang, Yan Wang, Leyang Cui, Shuming Shi, Zhaopeng Tu* Tencent AI Lab
Modeling discourse â the li... | {
"id": "2109.05729"
} |
2307.08072 | Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study | Despite the superior performance, Large Language Models~(LLMs) require
significant computational resources for deployment and use. To overcome this
issue, quantization methods have been widely applied to reduce the memory
footprint of LLMs as well as increasing the inference rate. However, a major
challenge is that low... | http://arxiv.org/pdf/2307.08072 | Peiyu Liu, Zikang Liu, Ze-Feng Gao, Dawei Gao, Wayne Xin Zhao, Yaliang Li, Bolin Ding, Ji-Rong Wen | cs.CL, cs.AI | 15 pages, 4 figures | null | cs.CL | 20230716 | 20230726 | 3 2 0 2
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Do Emergent Abilities Exist in Quantized Large Language Models: An Empirical Study Peiyu Liu1,2, Zikang Liu1,2, Ze-Feng Gao1, Dawei Gao3, Wayne Xin Zhao1,2â, Yaliang Li3, Bolin Ding3, and Ji-Rong Wen1,2,4 1 Gaoling School of Artiï¬cial Intelligence,... | {
"id": "2305.14314"
} |
2307.07924 | Communicative Agents for Software Development | Software engineering is a domain characterized by intricate decision-making
processes, often relying on nuanced intuition and consultation. Recent
advancements in deep learning have started to revolutionize software
engineering practices through elaborate designs implemented at various stages
of software development. I... | http://arxiv.org/pdf/2307.07924 | Chen Qian, Xin Cong, Wei Liu, Cheng Yang, Weize Chen, Yusheng Su, Yufan Dang, Jiahao Li, Juyuan Xu, Dahai Li, Zhiyuan Liu, Maosong Sun | cs.SE, cs.CL, cs.MA | https://github.com/OpenBMB/ChatDev | null | cs.SE | 20230716 | 20231219 | 3 2 0 2 c e D 9 1
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# Communicative Agents for Software Development
Chen Qian® XinCong® WeiLiu® Cheng Yang* Weize Chen® Yusheng Su® Yufan Dang* JiahaoLi* JuyuanXu4 DahaiLi* Zhiyuan Liué®⢠Maosong Sun®⢠*Tsinghua University Beijing University of Posts and Telec... | {
"id": "2204.06125"
} |
2307.11760 | Large Language Models Understand and Can be Enhanced by Emotional Stimuli | Emotional intelligence significantly impacts our daily behaviors and
interactions. Although Large Language Models (LLMs) are increasingly viewed as
a stride toward artificial general intelligence, exhibiting impressive
performance in numerous tasks, it is still uncertain if LLMs can genuinely
grasp psychological emotio... | http://arxiv.org/pdf/2307.11760 | Cheng Li, Jindong Wang, Yixuan Zhang, Kaijie Zhu, Wenxin Hou, Jianxun Lian, Fang Luo, Qiang Yang, Xing Xie | cs.CL, cs.AI, cs.HC | Technical report; updated the std error for human study; short
version (v1) was accepted by LLM@IJCAI'23; 32 pages; more work:
https://llm-enhance.github.io/ | null | cs.CL | 20230714 | 20231112 | 3 2 0 2
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# Large Language Models Understand and Can Be Enhanced by Emotional Stimuli
Cheng Li1, Jindong Wang2â, Yixuan Zhang3, Kaijie Zhu2, Wenxin Hou2, Jianxun Lian2, Fang Luo4, Qiang Yang5, Xing Xie2
1Institute of Software, CAS 2Microsoft 3William&Mary
# 4D... | {
"id": "2306.04528"
} |
2307.06135 | SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning | Large language models (LLMs) have demonstrated impressive results in
developing generalist planning agents for diverse tasks. However, grounding
these plans in expansive, multi-floor, and multi-room environments presents a
significant challenge for robotics. We introduce SayPlan, a scalable approach
to LLM-based, large... | http://arxiv.org/pdf/2307.06135 | Krishan Rana, Jesse Haviland, Sourav Garg, Jad Abou-Chakra, Ian Reid, Niko Suenderhauf | cs.RO, cs.AI | Accepted for oral presentation at the Conference on Robot Learning
(CoRL), 2023. Project page can be found here: https://sayplan.github.io | null | cs.RO | 20230712 | 20230927 | # SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning
Krishan Ranaâ 1, Jesse Havilandâ1,2, Sourav Gargâ3, Jad Abou-Chakraâ1, Ian Reid3, Niko S ¨underhauf1 1QUT Centre for Robotics, Queensland University of Technology 2CSIRO Data61 Robotics and Autonomous Systems Group... | {
"id": "2204.00598"
} |
2307.06290 | Instruction Mining: When Data Mining Meets Large Language Model Finetuning | Large language models (LLMs) are initially pretrained for broad capabilities
and then finetuned with instruction-following datasets to improve their
performance in interacting with humans. Despite advances in finetuning, a
standardized guideline for selecting high-quality datasets to optimize this
process remains elusi... | http://arxiv.org/pdf/2307.06290 | Yihan Cao, Yanbin Kang, Chi Wang, Lichao Sun | cs.CL, cs.AI, cs.LG | 22 pages, 7 figures | null | cs.CL | 20230712 | 20231027 | 3 2 0 2
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Preprint
INSTRUCTION MINING: WHEN DATA MINING MEETS LARGE LANGUAGE MODEL FINETUNING
Yihan Caoâ Carnegie Mellon University Pittsburgh, PA yihanc@cs.cmu.edu
Yanbin Kangâ LinkedIn Mountain View, CA ybkang@linkedin.com
Chi Wang Microsoft Research Redmo... | {
"id": "1905.07830"
} |
2307.06187 | Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems | In autonomic computing, self-adaptation has been proposed as a fundamental
paradigm to manage the complexity of multiagent systems (MASs). This achieved
by extending a system with support to monitor and adapt itself to achieve
specific concerns of interest. Communication in these systems is key given that
in scenarios ... | http://arxiv.org/pdf/2307.06187 | Nathalia Nascimento, Paulo Alencar, Donald Cowan | cs.MA, cs.AI, cs.CL | 6 pages, submitted | null | cs.MA | 20230712 | 20230712 | 3 2 0 2
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# Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems
Nathalia Nascimento, Paulo Alencar, Donald Cowan David R. Cheriton School of Computer Science University of Waterloo (UW) Waterloo, Canada {nmoraesd, palencar, dcowan} @uwaterloo.ca
Ab... | {
"id": "2210.11610"
} |
2307.06281 | MMBench: Is Your Multi-modal Model an All-around Player? | Large vision-language models have recently achieved remarkable progress,
exhibiting great perception and reasoning abilities concerning visual
information. However, how to effectively evaluate these large vision-language
models remains a major obstacle, hindering future model development.
Traditional benchmarks like VQ... | http://arxiv.org/pdf/2307.06281 | Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, Kai Chen, Dahua Lin | cs.CV, cs.CL | null | null | cs.CV | 20230712 | 20230813 | 3 2 0 2
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# MMBench: Is Your Multi-modal Model an All-around Player?
Yuan Liu1,â, Haodong Duan1,â, Yuanhan Zhang2,â, Bo Li2,â, Songyang Zhang1,â, Wangbo Zhao4, Yike Yuan5, Jiaqi Wang1, Conghui He1, Ziwei Liu2,â , Kai Chen1,â Dahua Lin1,3,â
# 1Shan... | {
"id": "2302.13971"
} |
2307.07526 | Can I say, now machines can think? | Generative AI techniques have opened the path for new generations of machines
in diverse domains. These machines have various capabilities for example, they
can produce images, generate answers or stories, and write codes based on the
"prompts" only provided by users. These machines are considered 'thinking
minds' beca... | http://arxiv.org/pdf/2307.07526 | Nitisha Aggarwal, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir | cs.AI, cs.CY, I.2.m Miscellaneous | 11 pages, 3 figures | null | cs.AI | 20230711 | 20230711 | 3 2 0 2
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CAN I SAY, NOW MACHINES CAN THINK?
Nitisha Aggarwal1, Geetika Jain Saxena2, Sanjeev Singh1, and Amit Pundir2
1Institute of Informatics & Communication, University of Delhi, Delhi, India 2Department of Electronics, Maharaja Agrasen College, University o... | {
"id": "1801.01957"
} |
2307.05300 | Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration | Human intelligence thrives on cognitive synergy, where collaboration among
different minds yield superior outcomes compared to isolated individuals. In
this work, we propose Solo Performance Prompting (SPP), which transforms a
single LLM into a cognitive synergist by engaging in multi-turn
self-collaboration with multi... | http://arxiv.org/pdf/2307.05300 | Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, Heng Ji | cs.AI, cs.CL | null | null | cs.AI | 20230711 | 20240104 | 4 2 0 2
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# Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration
Zhenhailong Wang1â, Shaoguang Mao2, Wenshan Wu2, Tao Ge2, Furu Wei2, Heng Ji1 1University of Illinois Urbana-Champaign, ... | {
"id": "2302.06476"
} |
2307.04964 | Secrets of RLHF in Large Language Models Part I: PPO | Large language models (LLMs) have formulated a blueprint for the advancement
of artificial general intelligence. Its primary objective is to function as a
human-centric (helpful, honest, and harmless) assistant. Alignment with humans
assumes paramount significance, and reinforcement learning with human feedback
(RLHF) ... | http://arxiv.org/pdf/2307.04964 | Rui Zheng, Shihan Dou, Songyang Gao, Yuan Hua, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Zhu, Cheng Chang, Zhangyue Yin, Rongxiang Weng, Wensen Cheng, Haoran Huang, Tianxiang Sun, Hang Yan, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huan... | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230711 | 20230718 | 3 2 0 2
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# Secrets of RLHF in Large Language Models Part I: PPO
# Rui Zhengâ â , Shihan Douââ , Songyang Gaoâ,
Yuan Huaâ¡, Wei Shen, Binghai Wang, Yan Liu, Senjie Jin, Qin Liu, Yuhao Zhou, Limao Xiong, Lu Chen, Zhiheng Xi, Nuo Xu, Wenbin Lai, Minghao Z... | {
"id": "2302.13971"
} |
2307.04738 | RoCo: Dialectic Multi-Robot Collaboration with Large Language Models | We propose a novel approach to multi-robot collaboration that harnesses the
power of pre-trained large language models (LLMs) for both high-level
communication and low-level path planning. Robots are equipped with LLMs to
discuss and collectively reason task strategies. They then generate sub-task
plans and task space ... | http://arxiv.org/pdf/2307.04738 | Zhao Mandi, Shreeya Jain, Shuran Song | cs.RO, cs.AI, cs.LG | null | null | cs.RO | 20230710 | 20230710 | 3 2 0 2
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# RoCo: Dialectic Multi-Robot Collaboration with Large Language Models
Zhao Mandi Columbia University
Shreeya Jain Columbia University
Shuran Song Columbia University
Abstract: We propose a novel approach to multi-robot collaboration that har- nesses t... | {
"id": "2210.06407"
} |
2307.04657 | BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset | In this paper, we introduce the BeaverTails dataset, aimed at fostering
research on safety alignment in large language models (LLMs). This dataset
uniquely separates annotations of helpfulness and harmlessness for
question-answering pairs, thus offering distinct perspectives on these crucial
attributes. In total, we ha... | http://arxiv.org/pdf/2307.04657 | Jiaming Ji, Mickel Liu, Juntao Dai, Xuehai Pan, Chi Zhang, Ce Bian, Chi Zhang, Ruiyang Sun, Yizhou Wang, Yaodong Yang | cs.CL | Published at NeurIPS 2023 | null | cs.CL | 20230710 | 20231107 | 3 2 0 2
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# BEAVERTAILS: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset
# Jiaming Ji*1 Mickel Liu*2 Juntao Dai*1 Xuehai Pan2 Chi Zhang1
# Ce Bian! BoyuanChen! RuiyangSun! Yizhou Wangâ¢!? Yaodong Yang =!
1Institute for Artificial Intellige... | {
"id": "2302.05206"
} |
2307.07522 | The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence | Recent advances in machine learning and AI, including Generative AI and LLMs,
are disrupting technological innovation, product development, and society as a
whole. AI's contribution to technology can come from multiple approaches that
require access to large training data sets and clear performance evaluation
criteria,... | http://arxiv.org/pdf/2307.07522 | Hector Zenil, Jesper Tegnér, Felipe S. Abrahão, Alexander Lavin, Vipin Kumar, Jeremy G. Frey, Adrian Weller, Larisa Soldatova, Alan R. Bundy, Nicholas R. Jennings, Koichi Takahashi, Lawrence Hunter, Saso Dzeroski, Andrew Briggs, Frederick D. Gregory, Carla P. Gomes, Jon Rowe, James Evans, Hiroaki Kitano, Ross King | cs.AI, cs.LG | 35 pages, first draft of the final report from the Alan Turing
Institute on AI for Scientific Discovery | null | cs.AI | 20230709 | 20230829 | 3 2 0 2
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# The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
1
Hector Zenil,1,2,3,4,â Jesper Tegn´er,21,27 Felipe S. AbrahËao,3,4,8,26, Alexander Lavin,19,20 Vipin Kumar,6 Jeremy G. Frey,7 Adrian Weller,1,2 Larisa Solda... | {
"id": "2003.11755"
} |
2307.03875 | Large Language Models for Supply Chain Optimization | Supply chain operations traditionally involve a variety of complex decision
making problems. Over the last few decades, supply chains greatly benefited
from advances in computation, which allowed the transition from manual
processing to automation and cost-effective optimization. Nonetheless, business
operators still n... | http://arxiv.org/pdf/2307.03875 | Beibin Li, Konstantina Mellou, Bo Zhang, Jeevan Pathuri, Ishai Menache | cs.AI, cs.CL, cs.DM, cs.LG | null | null | cs.AI | 20230708 | 20230713 | 3 2 0 2
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# Large Language Models for Supply Chain Optimization
Beibin Li1, Konstantina Mellou1, Bo Zhang2, Jeevan Pathuri2, and Ishai Menache1
1Microsoft Research 2Microsoft Cloud Supply Chain
# Abstract
Supply chain operations traditionally involve a variety o... | {
"id": "2302.13971"
} |
2307.03762 | Brain in a Vat: On Missing Pieces Towards Artificial General Intelligence in Large Language Models | In this perspective paper, we first comprehensively review existing
evaluations of Large Language Models (LLMs) using both standardized tests and
ability-oriented benchmarks. We pinpoint several problems with current
evaluation methods that tend to overstate the capabilities of LLMs. We then
articulate what artificial ... | http://arxiv.org/pdf/2307.03762 | Yuxi Ma, Chi Zhang, Song-Chun Zhu | cs.CL, cs.AI | null | null | cs.CL | 20230707 | 20230707 | 3 2 0 2
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# Brain in a Vat: On Missing Pieces Towards Artiï¬cial General Intelligence in Large Language Models
# Yuxi Ma1*, Chi Zhang1, Song-Chun Zhu1,2
# 1Beijing Insitute for General Artiï¬cial Intelligence (BIGAI) 2Peking University
In this perspective paper... | {
"id": "2305.15068"
} |
2307.03172 | Lost in the Middle: How Language Models Use Long Contexts | While recent language models have the ability to take long contexts as input,
relatively little is known about how well they use longer context. We analyze
the performance of language models on two tasks that require identifying
relevant information in their input contexts: multi-document question answering
and key-val... | http://arxiv.org/pdf/2307.03172 | Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, Percy Liang | cs.CL | 18 pages, 16 figures. Accepted for publication in Transactions of the
Association for Computational Linguistics (TACL), 2023 | null | cs.CL | 20230706 | 20231120 | 3 2 0 2
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# Lost in the Middle: How Language Models Use Long Contexts
Nelson F. Liu1â Kevin Lin2 Michele Bevilacqua3 John Hewitt1 Fabio Petroni3 2University of California, Berkeley nfliu@cs.stanford.edu Ashwin Paranjape3 Percy Liang1 1Stanford University 3Sama... | {
"id": "2302.13971"
} |
2307.03109 | A Survey on Evaluation of Large Language Models | Large language models (LLMs) are gaining increasing popularity in both
academia and industry, owing to their unprecedented performance in various
applications. As LLMs continue to play a vital role in both research and daily
use, their evaluation becomes increasingly critical, not only at the task
level, but also at th... | http://arxiv.org/pdf/2307.03109 | Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie | cs.CL, cs.AI | Accepted by ACM Transactions on Intelligent Systems and Technology
(TIST); 45 pages; More recent works; https://llm-eval.github.io/ | null | cs.CL | 20230706 | 20231229 | 3 2 0 2 c e D 9 2
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# A Survey on Evaluation of Large Language Models
YUPENG CHANGâ and XU WANGâ, School of Artificial Intelligence, Jilin University, China JINDONG WANGâ , Microsoft Research Asia, China YUAN WUâ , School of Artificial Intelligence, Jilin University... | {
"id": "2212.13138"
} |
2307.02762 | PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations | Nowadays, the quality of responses generated by different modern large
language models (LLMs) are hard to evaluate and compare automatically. Recent
studies suggest and predominantly use LLMs as a reference-free metric for
open-ended question answering. More specifically, they use the recognized
"strongest" LLM as the ... | http://arxiv.org/pdf/2307.02762 | Ruosen Li, Teerth Patel, Xinya Du | cs.CL, cs.AI | null | null | cs.CL | 20230706 | 20230706 | 3 2 0 2
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# PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations
Teerth Patelâ Department of Computer Science The University of Texas at Dallas {ruosen.li, teerth.patel, xinya.du}@utdallas.edu
# Abstract
Nowadays, the quality of responses... | {
"id": "1803.05457"
} |
2307.03692 | Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning | In this paper, we introduce the Instruction Following Score (IFS), a metric
that detects language models' ability to follow instructions. The metric has a
dual purpose. First, IFS can be used to distinguish between base and instruct
models. We benchmark publicly available base and instruct models, and show that
the rat... | http://arxiv.org/pdf/2307.03692 | Waseem AlShikh, Manhal Daaboul, Kirk Goddard, Brock Imel, Kiran Kamble, Parikshith Kulkarni, Melisa Russak | cs.CL, cs.AI | null | null | cs.CL | 20230705 | 20230705 | 3 2 0 2
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# Becoming self-instruct: introducing early stopping criteria for minimal instruct tuning
# Waseem AlShikh Manhal Daaboul Kirk Goddard Brock Imel Kiran Kamble
Parikshith Kulkarni
Melisa Russak
Writer, Inc. {waseem,...,melisa}@writer.com
# Abstract
In thi... | {
"id": "2101.00027"
} |
2307.02485 | Building Cooperative Embodied Agents Modularly with Large Language Models | Large Language Models (LLMs) have demonstrated impressive planning abilities
in single-agent embodied tasks across various domains. However, their capacity
for planning and communication in multi-agent cooperation remains unclear, even
though these are crucial skills for intelligent embodied agents. In this paper,
we p... | http://arxiv.org/pdf/2307.02485 | Hongxin Zhang, Weihua Du, Jiaming Shan, Qinhong Zhou, Yilun Du, Joshua B. Tenenbaum, Tianmin Shu, Chuang Gan | cs.AI, cs.CL, cs.CV | Project page: https://vis-www.cs.umass.edu/Co-LLM-Agents/ | null | cs.AI | 20230705 | 20230705 | 3 2 0 2
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# Building Cooperative Embodied Agents Modularly with Large Language Models
Hongxin Zhang1â, Weihua Du2â, Jiaming Shan3, Qinhong Zhou1 Yilun Du4, Joshua B. Tenenbaum4, Tianmin Shu4, Chuang Gan1,5
1University of Massachusetts Amherst, 2 Tsinghua Unive... | {
"id": "2211.09935"
} |
2307.02053 | Flacuna: Unleashing the Problem Solving Power of Vicuna using FLAN Fine-Tuning | Recently, the release of INSTRUCTEVAL has provided valuable insights into the
performance of large language models (LLMs) that utilize encoder-decoder or
decoder-only architecture. Interestingly, despite being introduced four years
ago, T5-based LLMs, such as FLAN-T5, continue to outperform the latest
decoder-based LLM... | http://arxiv.org/pdf/2307.02053 | Deepanway Ghosal, Yew Ken Chia, Navonil Majumder, Soujanya Poria | cs.CL | null | null | cs.CL | 20230705 | 20230705 | 3 2 0 2
l u J 5 ] L C . s c [
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# FLACUNA: Unleashing the Problem Solving Power of VICUNA using FLAN Fine-Tuning
Deepanway Ghosalâ¡, Yew Ken Chiaâ¡, Navonil Majumderâ , Soujanya Poriaâ¡ â¡ DeCLaRe Lab, Singapore University of Technology and Design, Singapore {deepanway_ghosal, yew... | {
"id": "2301.13688"
} |
2307.02046 | Recommender Systems in the Era of Large Language Models (LLMs) | With the prosperity of e-commerce and web applications, Recommender Systems
(RecSys) have become an important component of our daily life, providing
personalized suggestions that cater to user preferences. While Deep Neural
Networks (DNNs) have made significant advancements in enhancing recommender
systems by modeling ... | http://arxiv.org/pdf/2307.02046 | Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang, Qing Li | cs.IR, cs.AI, cs.CL | 16 pages, 5 figures | null | cs.IR | 20230705 | 20230805 | 3 2 0 2 g u A 5 ] R I . s c [
2 v 6 4 0 2 0 . 7 0 3 2 : v i X r a
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, SUBMISSION 2023
# Recommender Systems in the Era of Large Language Models (LLMs)
Wenqi Fan, Zihuai Zhao, Jiatong Li, Yunqing Liu, Xiaowei Mei, Yiqi Wang, Zhen Wen, Fei Wang, Xiangyu Zhao, Jiliang Tang,... | {
"id": "2201.11903"
} |
2307.01848 | Embodied Task Planning with Large Language Models | Equipping embodied agents with commonsense is important for robots to
successfully complete complex human instructions in general environments.
Recent large language models (LLM) can embed rich semantic knowledge for agents
in plan generation of complex tasks, while they lack the information about the
realistic world a... | http://arxiv.org/pdf/2307.01848 | Zhenyu Wu, Ziwei Wang, Xiuwei Xu, Jiwen Lu, Haibin Yan | cs.CV, cs.AI, cs.RO | Project Page: https://gary3410.github.io/TaPA | null | cs.CV | 20230704 | 20230704 | 3 2 0 2
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# Embodied Task Planning with Large Language Models
Zhenyu Wu1, Ziwei Wang2,3, Xiuwei Xu2,3, Jiwen Lu2,3, Haibin Yan1â 1School of Automation, Beijing University of Posts and Telecommunications, China 2Department of Automation, Tsinghua University, Chin... | {
"id": "2302.13971"
} |
2307.02502 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics | The advancement in generative AI could be boosted with more accessible
mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in
programming, algorithm discovery, and theorem proving, yet their genomics
application is limited. This project introduces Math Agents and mathematical
embedding as fresh... | http://arxiv.org/pdf/2307.02502 | Melanie Swan, Takashi Kido, Eric Roland, Renato P. dos Santos | q-bio.OT, cs.AI, cs.CL, 68R12, I.2; J.3 | null | null | q-bio.OT | 20230704 | 20230704 | Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics Melanie Swan,a Takashi Kido,b Eric Roland,c Renato P. dos Santosd
aDIYgenomics.org; University College London (Research Associate) bAdvanced Comprehensive Research Organization, Teikyo University; Preferred Networks, Inc. cRedBud AI, LLC dC... | {
"id": "1601.00257"
} |
2307.01135 | ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience | The advent of ChatGPT, a large language model-powered chatbot, has prompted
questions about its potential implications for traditional search engines. In
this study, we investigate the differences in user behavior when employing
search engines and chatbot tools for information-seeking tasks. We carry out a
randomized o... | http://arxiv.org/pdf/2307.01135 | Ruiyun Xu, Yue Feng, Hailiang Chen | cs.AI, cs.HC, cs.IR | 30 pages, 5 figures, 2 tables | null | cs.AI | 20230703 | 20230703 | # ChatGPT vs. Google: A Comparative Study of Search Performance and User Experience
Ruiyun (Rayna) Xu, Yue (Katherine) Feng, and Hailiang Chen*
July 2023
# Abstract
The advent of ChatGPT, a large language model-powered chatbot, has prompted questions about its potential implications for traditional search engines. In t... | {
"id": "2304.07619"
} |
2307.00112 | Performance of ChatGPT on USMLE: Unlocking the Potential of Large Language Models for AI-Assisted Medical Education | Artificial intelligence is gaining traction in more ways than ever before.
The popularity of language models and AI-based businesses has soared since
ChatGPT was made available to the general public via OpenAI. It is becoming
increasingly common for people to use ChatGPT both professionally and
personally. Considering ... | http://arxiv.org/pdf/2307.00112 | Prabin Sharma, Kisan Thapa, Dikshya Thapa, Prastab Dhakal, Mala Deep Upadhaya, Santosh Adhikari, Salik Ram Khanal | cs.CY, cs.AI | 12 pages, 4 Figues, 4 tables | null | cs.CY | 20230630 | 20230727 | # Performance of ChatGPT on USMLE: Unlocking the Potential of Large Language Models for AI-Assisted Medical Education
Prabin Sharma1, Kisan Thapa1, Prastab Dhakal 2, Mala Deep Upadhaya3, Dikshya Thapa1, Santosh Adhikari4, Salik Ram Khanal5
1 University of Massachusetts Boston, USA 2 Texas Tech University, USA 3 Coventr... | {
"id": "2005.14165"
} |
2306.17563 | Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting | Ranking documents using Large Language Models (LLMs) by directly feeding the
query and candidate documents into the prompt is an interesting and practical
problem. However, there has been limited success so far, as researchers have
found it difficult to outperform fine-tuned baseline rankers on benchmark
datasets. We a... | http://arxiv.org/pdf/2306.17563 | Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, Michael Bendersky | cs.IR, cs.CL, cs.LG | 12 pages, 3 figures | null | cs.IR | 20230630 | 20230630 | 3 2 0 2 n u J 0 3 ] R I . s c [
1 v 3 6 5 7 1 . 6 0 3 2 : v i X r a
arXiv:2306.17563v1
# Preprint
LARGE LANGUAGE MODELS ARE EFFECTIVE TEXT RANKERS WITH PAIRWISE RANKING PROMPTING
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, Michael Bender... | {
"id": "2204.02311"
} |
2306.17492 | Preference Ranking Optimization for Human Alignment | Large language models (LLMs) often contain misleading content, emphasizing
the need to align them with human values to ensure secur AI systems.
Reinforcement learning from human feedback (RLHF) has been employed to achieve
this alignment by combining a reward model, typically based on Bradley-Terry
paired comparison, w... | http://arxiv.org/pdf/2306.17492 | Feifan Song, Bowen Yu, Minghao Li, Haiyang Yu, Fei Huang, Yongbin Li, Houfeng Wang | cs.CL, cs.AI | null | null | cs.CL | 20230630 | 20230630 | 3 2 0 2
n u J 0 3 ] L C . s c [
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# Preference Ranking Optimization for Human Alignment
Feifan Song1, Bowen Yu2â, Minghao Li2 Haiyang Yu2, Fei Huang2, Yongbin Li2, Houfeng Wang1â 1National Key Laboratory of Multimedia Information Processing, Peking University 2Alibaba Group songff@... | {
"id": "2302.13971"
} |
2306.17107 | LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding | Instruction tuning unlocks the superior capability of Large Language Models
(LLM) to interact with humans. Furthermore, recent instruction-following
datasets include images as visual inputs, collecting responses for image-based
instructions. However, visual instruction-tuned models cannot comprehend
textual details wit... | http://arxiv.org/pdf/2306.17107 | Yanzhe Zhang, Ruiyi Zhang, Jiuxiang Gu, Yufan Zhou, Nedim Lipka, Diyi Yang, Tong Sun | cs.CV, cs.CL | Preprint. Work in progress | null | cs.CV | 20230629 | 20230629 | 3 2 0 2
n u J 9 2 ] V C . s c [
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# LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding
Yanzhe Zhang1â, Ruiyi Zhang2, Jiuxiang Gu2, Yufan Zhou2, Nedim Lipka2, Diyi Yang3, Tong Sun2 1Georgia Tech, 2Adobe Research, 3Stanford University
# Abstract
Instruction tu... | {
"id": "2306.02858"
} |
2306.16803 | Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis | To make reinforcement learning more sample efficient, we need better credit
assignment methods that measure an action's influence on future rewards.
Building upon Hindsight Credit Assignment (HCA), we introduce Counterfactual
Contribution Analysis (COCOA), a new family of model-based credit assignment
algorithms. Our a... | http://arxiv.org/pdf/2306.16803 | Alexander Meulemans, Simon Schug, Seijin Kobayashi, Nathaniel Daw, Gregory Wayne | cs.LG, stat.ML | NeurIPS 2023 spotlight | null | cs.LG | 20230629 | 20231031 | 3 2 0 2
t c O 1 3 ] G L . s c [
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# Would I have gotten that reward? Long-term credit assignment by counterfactual contribution analysis
# Alexander Meulemansâ1, Simon Schugâ1, Seijin Kobayashiâ1 Nathaniel D Daw2,3,4, Gregory Wayne2
1Department of Computer Science, ETH Zürich 2G... | {
"id": "1912.02875"
} |
2306.16636 | CMATH: Can Your Language Model Pass Chinese Elementary School Math Test? | We present the Chinese Elementary School Math Word Problems (CMATH) dataset,
comprising 1.7k elementary school-level math word problems with detailed
annotations, source from actual Chinese workbooks and exams. This dataset aims
to provide a benchmark tool for assessing the following question: to what grade
level of el... | http://arxiv.org/pdf/2306.16636 | Tianwen Wei, Jian Luan, Wei Liu, Shuang Dong, Bin Wang | cs.CL, cs.AI, cs.LG | null | null | cs.CL | 20230629 | 20230629 | 3 2 0 2
n u J 9 2 ] L C . s c [
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# CMATH: Can Your Language Model Pass Chinese Elementary School Math Test?
Tianwen Wei Jian Luan Wei Liu Shuang Dong Bin Wang Xiaomi AI Lab
weitianwen,luanjian,liuwei40,dongshuang1,wangbin11 @xiaomi.com
{
}
# Abstract
We present the Chinese Elementary ... | {
"id": "2210.02414"
} |
2306.16564 | Automatic Calibration and Error Correction for Generative Large Language Models via Pareto Optimal Self-Supervision | Generative Large language models (LLMs) have demonstrated remarkable
capabilities for a wide range of applications, but reducing ungrounded or
erroneous responses remains a major growth area. Unlike task-specific models,
there lack an effective method to calibrate the confidence level of LLM
responses to indicate poten... | http://arxiv.org/pdf/2306.16564 | Theodore Zhao, Mu Wei, J. Samuel Preston, Hoifung Poon | cs.CL, stat.ML | null | null | cs.CL | 20230628 | 20231026 | 3 2 0 2
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# Automatic Calibration and Error Correction for Generative Large Language Models via Pareto Optimal Self-Supervision
Theodore Zhao Microsoft Mu Wei Microsoft J. Samuel Preston Microsoft Hoifung Poon Microsoft
# Abstract
Generative Large language model... | {
"id": "1808.08485"
} |
2306.16092 | ChatLaw: Open-Source Legal Large Language Model with Integrated External Knowledge Bases | Large Language Models (LLMs) have shown the potential to revolutionize
natural language processing tasks in various domains, sparking great interest
in vertical-specific large models. However, unlike proprietary models such as
BloombergGPT and FinGPT, which have leveraged their unique data accumulations
to make strides... | http://arxiv.org/pdf/2306.16092 | Jiaxi Cui, Zongjian Li, Yang Yan, Bohua Chen, Li Yuan | cs.CL | null | null | cs.CL | 20230628 | 20230628 | 3 2 0 2
n u J 8 2 ] L C . s c [
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# ChatLaw: Open-Source Legal Large Language Model with Integrated External Knowledge Bases
# Jiaxi Cuiâ Peking University jiaxicui@chatlaw.cloud
Zongjian Liâ Peking University chestnutlzj@chatlaw.cloud
# Yang Yan Peking University yyang@stu.pku.edu... | {
"id": "2302.13971"
} |
2306.15895 | Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias | Large language models (LLMs) have been recently leveraged as training data
generators for various natural language processing (NLP) tasks. While previous
research has explored different approaches to training models using generated
data, they generally rely on simple class-conditional prompts, which may limit
the diver... | http://arxiv.org/pdf/2306.15895 | Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander Ratner, Ranjay Krishna, Jiaming Shen, Chao Zhang | cs.CL, cs.AI, cs.LG | Accepted to NeurIPS 2023 (Datasets and Benchmarks Track) | NeurIPS 2023 | cs.CL | 20230628 | 20231018 | 3 2 0 2
t c O 8 1 ] L C . s c [
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# Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias
Yue Yu1â, Yuchen Zhuang1â, Jieyu Zhang2â, Yu Meng3, Alexander Ratner2, Ranjay Krishna2, Jiaming Shen4, Chao Zhang1 1 Georgia Institute of Technology 2 Univ... | {
"id": "2302.04023"
} |
2306.15626 | LeanDojo: Theorem Proving with Retrieval-Augmented Language Models | Large language models (LLMs) have shown promise in proving formal theorems
using proof assistants such as Lean. However, existing methods are difficult to
reproduce or build on, due to private code, data, and large compute
requirements. This has created substantial barriers to research on machine
learning methods for t... | http://arxiv.org/pdf/2306.15626 | Kaiyu Yang, Aidan M. Swope, Alex Gu, Rahul Chalamala, Peiyang Song, Shixing Yu, Saad Godil, Ryan Prenger, Anima Anandkumar | cs.LG, cs.AI, cs.LO, stat.ML | Accepted to NeurIPS 2023 (Datasets and Benchmarks Track) as an oral
presentation. Data, code, and models available at https://leandojo.org/ | null | cs.LG | 20230627 | 20231027 | 3 2 0 2
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# LeanDojo: Theorem Proving with Retrieval-Augmented Language Models
Kaiyu Yang1, Aidan M. Swope2, Alex Gu3, Rahul Chalamala1, Peiyang Song4, Shixing Yu5, Saad Godilâ, Ryan Prenger2, Anima Anandkumar1,2 1Caltech, 2NVIDIA, 3MIT, 4UC Santa Barbara, 5UT... | {
"id": "2302.13971"
} |
2306.15595 | Extending Context Window of Large Language Models via Positional Interpolation | We present Position Interpolation (PI) that extends the context window sizes
of RoPE-based pretrained LLMs such as LLaMA models to up to 32768 with minimal
fine-tuning (within 1000 steps), while demonstrating strong empirical results
on various tasks that require long context, including passkey retrieval,
language mode... | http://arxiv.org/pdf/2306.15595 | Shouyuan Chen, Sherman Wong, Liangjian Chen, Yuandong Tian | cs.CL, cs.AI, cs.LG | Fix template issues | null | cs.CL | 20230627 | 20230628 | 3 2 0 2 n u J 8 2 ] L C . s c [
2 v 5 9 5 5 1 . 6 0 3 2 : v i X r a
# EXTENDING CONTEXT WINDOW OF LARGE LAN- GUAGE MODELS VIA POSITION INTERPOLATION
# Shouyuan Chen Meta Platforms Inc. {chenshouyuan,shermanwong,clj,yuandong}@meta.com
# Tian
# ABSTRACT
We present Position Interpolation (PI) that extends the context wind... | {
"id": "2101.00027"
} |
2306.15222 | Learning to Rank in Generative Retrieval | Generative retrieval stands out as a promising new paradigm in text retrieval
that aims to generate identifier strings of relevant passages as the retrieval
target. This generative paradigm taps into powerful generative language models,
distinct from traditional sparse or dense retrieval methods. However, only
learning... | http://arxiv.org/pdf/2306.15222 | Yongqi Li, Nan Yang, Liang Wang, Furu Wei, Wenjie Li | cs.CL, cs.AI, cs.IR | AAAI 2024 | null | cs.CL | 20230627 | 20231216 | 3 2 0 2 c e D 6 1
] L C . s c [
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# Learning to Rank in 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
# Abstract
Gener... | {
"id": "2207.02578"
} |
2306.15195 | Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic | In human conversations, individuals can indicate relevant regions within a
scene while addressing others. In turn, the other person can then respond by
referring to specific regions if necessary. This natural referential ability in
dialogue remains absent in current Multimodal Large Language Models (MLLMs). To
fill thi... | http://arxiv.org/pdf/2306.15195 | Keqin Chen, Zhao Zhang, Weili Zeng, Richong Zhang, Feng Zhu, Rui Zhao | cs.CV | null | null | cs.CV | 20230627 | 20230703 | 3 2 0 2
l u J 3 ] V C . s c [
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# Shikra: Unleashing Multimodal LLMâs Referential Dialogue Magic
Keqin Chen12â, Zhao Zhang1â , Weili Zeng3, Richong Zhang2, Feng Zhu1, Rui Zhao14 1SenseTime Research; 2SKLSDE, Beihang University {3SEIEE, 4Qing Yuan Research Institute}, Shanghai Jiao... | {
"id": "2304.02643"
} |
2306.14824 | Kosmos-2: Grounding Multimodal Large Language Models to the World | We introduce Kosmos-2, a Multimodal Large Language Model (MLLM), enabling new
capabilities of perceiving object descriptions (e.g., bounding boxes) and
grounding text to the visual world. Specifically, we represent refer
expressions as links in Markdown, i.e., ``[text span](bounding boxes)'', where
object descriptions ... | http://arxiv.org/pdf/2306.14824 | Zhiliang Peng, Wenhui Wang, Li Dong, Yaru Hao, Shaohan Huang, Shuming Ma, Furu Wei | cs.CL, cs.CV | 20 pages | null | cs.CL | 20230626 | 20230713 | 3 2 0 2
l u J 3 1 ] L C . s c [
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# KOSMOS-2: Grounding Multimodal Large Language Models to the World
Zhiliang Pengâ, Wenhui Wangâ, Li Dongâ, Yaru Hao, Shaohan Huang, Shuming Ma, Furu Weiâ Microsoft Research https://aka.ms/GeneralAI
# Abstract
We introduce KOSMOS-2, a Multimoda... | {
"id": "2301.13688"
} |
2306.14898 | InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback | Humans write code in a fundamentally interactive manner and rely on constant
execution feedback to correct errors, resolve ambiguities, and decompose tasks.
While LLMs have recently exhibited promising coding capabilities, current
coding benchmarks mostly consider a static instruction-to-code sequence
transduction proc... | http://arxiv.org/pdf/2306.14898 | John Yang, Akshara Prabhakar, Karthik Narasimhan, Shunyu Yao | cs.CL, cs.LG, cs.SE | Project site with code and data:
https://intercode-benchmark.github.io | null | cs.CL | 20230626 | 20231030 | 3 2 0 2
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# InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback
# John Yang Akshara Prabhakar Karthik Narasimhan Shunyu Yao
Department of Computer Science, Princeton University {jy1682, ap5697, karthikn, shunyuy}@princeton.edu
# ... | {
"id": "2304.05128"
} |
2306.14565 | Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning | Despite the promising progress in multi-modal tasks, current large
multi-modal models (LMMs) are prone to hallucinating inconsistent descriptions
with respect to the associated image and human instructions. This paper
addresses this issue by introducing the first large and diverse visual
instruction tuning dataset, nam... | http://arxiv.org/pdf/2306.14565 | Fuxiao Liu, Kevin Lin, Linjie Li, Jianfeng Wang, Yaser Yacoob, Lijuan Wang | cs.CV, cs.AI, cs.CE, cs.CL, cs.MM | 40 pages, 32 figures. Under Review | null | cs.CV | 20230626 | 20230929 | 3 2 0 2
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# Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
Fuxiao Liu1, Kevin Lin2, Linjie Li2, Jianfeng Wang2, Yaser Yacoob1, Lijuan Wang2 1University of Maryland, College Park 2Microsoft Corporation {fl3es, yaser}@umd.edu, {... | {
"id": "2307.05052"
} |
2306.13421 | Long-range Language Modeling with Self-retrieval | Retrieval-augmented language models (LMs) have received much attention
recently. However, typically the retriever is not trained jointly as a native
component of the LM, but added to an already-pretrained LM, which limits the
ability of the LM and the retriever to adapt to one another. In this work, we
propose the Retr... | http://arxiv.org/pdf/2306.13421 | Ohad Rubin, Jonathan Berant | cs.CL | null | null | cs.CL | 20230623 | 20230623 | 3 2 0 2
n u J 3 2 ] L C . s c [
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# Long-range Language Modeling with Self-retrieval
# Ohad Rubin Jonathan Berant
# The Blavatnik School of Computer Science, Tel Aviv University {ohad.rubin,joberant}@cs.tau.ac.il
# Abstract
Retrieval-augmented language models (LMs) have received much a... | {
"id": "2004.05150"
} |
2306.13304 | ToolQA: A Dataset for LLM Question Answering with External Tools | Large Language Models (LLMs) have demonstrated impressive performance in
various NLP tasks, but they still suffer from challenges such as hallucination
and weak numerical reasoning. To overcome these challenges, external tools can
be used to enhance LLMs' question-answering abilities. However, current
evaluation method... | http://arxiv.org/pdf/2306.13304 | Yuchen Zhuang, Yue Yu, Kuan Wang, Haotian Sun, Chao Zhang | cs.CL, cs.AI | null | null | cs.CL | 20230623 | 20230623 | 3 2 0 2
n u J 3 2 ] L C . s c [
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# ToolQA: A Dataset for LLM Question Answering with External Tools
Yuchen Zhuangâ, Yue Yuâ, Kuan Wangâ, Haotian Sun, Chao Zhang College of Computing, Georgia Institute of Technology, Atlanta GA {yczhuang, yueyu, kuanwang, haotian.sun, chaozhang}@... | {
"id": "2302.13971"
} |
2306.12672 | From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought | How does language inform our downstream thinking? In particular, how do
humans make meaning from language--and how can we leverage a theory of
linguistic meaning to build machines that think in more human-like ways? In
this paper, we propose rational meaning construction, a computational framework
for language-informed... | http://arxiv.org/pdf/2306.12672 | Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, Joshua B. Tenenbaum | cs.CL, cs.AI, cs.SC | null | null | cs.CL | 20230622 | 20230623 | 3 2 0 2 n u J 3 2
# ] L C . s c [
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# From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought
Lionel Wong1â, Gabriel Grand1â, Alexander K. Lew1, Noah D. Goodman2, Vikash K. Mansinghka1, Jacob Andreas1, Joshua B. Tenenbaum1
âEqu... | {
"id": "1810.04805"
} |
2306.12420 | LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models | Large foundation models have demonstrated a great ability to achieve general
human-level intelligence far beyond traditional approaches. As the technique
keeps attracting attention from the AI community, more and more large
foundation models have become publically available. However, most of those
models exhibit a majo... | http://arxiv.org/pdf/2306.12420 | Shizhe Diao, Rui Pan, Hanze Dong, Ka Shun Shum, Jipeng Zhang, Wei Xiong, Tong Zhang | cs.CL, cs.AI | 13 pages, 3 figures | null | cs.CL | 20230621 | 20230621 | 3 2 0 2
n u J 1 2 ] L C . s c [
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# LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models
# Shizhe Diaoâ Rui Panâ Hanze Dongâ Ka Shun Shum Jipeng Zhang Wei Xiong
# Tong Zhang
# Abstract
Large foundation models have demonstrated a great ability to ... | {
"id": "2302.13971"
} |
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