| --- |
| license: llama2 |
| datasets: |
| - ACE05 |
| - conll2003 |
| - conll2012_ontonotesv5 |
| - rams |
| - tacred |
| - fewrel |
| - maven |
| language: |
| - en |
| metrics: |
| - f1 |
| pipeline_tag: text-generation |
| tags: |
| - text-generation-inference |
| - Information Extraction |
| - IE |
| - Named Entity Recogniton |
| - Event Extraction |
| - Relation Extraction |
| - LLaMA |
| --- |
| |
| # Model Card for ADELIE-SFT-3B |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
|
|
| <p align="justify"> |
| We introduce <b>ADELIE</b> (<b>A</b>ligning large language mo<b>DEL</b>s on <b>I</b>nformation <b>E</b>xtraction), an aligned LLM that effectively solves various IE tasks, including closed IE, open IE, and on-demand IE. We first collect and construct a high-quality alignment corpus <font face="Verdana">IEInstruct</font> for IE. Then we train ADELIE<sub>SFT</sub> using instruction tuning on <font face="Verdana">IEInstruct</font>. We further train ADELIE<sub>SFT</sub> with direct preference optimization (DPO) objective, resulting in ADELIE<sub>DPO</sub>. Extensive experiments on various held-out IE datasets demonstrate that our models (ADELIE<sub>SFT</sub> and ADELIE<sub>DPO</sub>) achieve state-of-the-art (SoTA) performance among open-source models. We further explore the general capabilities of ADELIE, and experimental results reveal that their general capabilities do not exhibit a noticeable decline. |
|
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| - 📖 Paper: [ADELIE: Aligning Large Language Models on Information Extraction](https://arxiv.org/abs/2405.05008) |
| </p> |
| - 🐧 Github: [THU/ADELIE](https://github.com/THU-KEG/ADELIE/tree/main) |
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|
| # Model Performance |
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| The table below presents the average F1 scores (%) of the ADELIE model across closed IE, open IE, and on-demand IE tasks, as well as its overall performance (%) on general benchmarks. For dataset details, please refer to the paper. |
|
|
| | Model | Closed IE | Open IE | On-demand IE | General Average Score | |
| |-----------------|-----------|---------|--------------|-----------------------| |
| | Llama2 7B | 5.7 | 5.6 | 22.4 | 52.2 | |
| | ADELIE-SFT | 42.6 | 46.9 | 60.4 | 53.5 | |
| | ADELIE-DPO | **42.7** | **47.6** | **60.5** | **53.8** | |
| |-----------------|-----------|---------|--------------|-----------------------| |
| | Llama3.2 3B | 19.1 | 18.5 | 20.8 | 55.5 | |
| | ADELIE-SFT-3B | **41.8** | 47.6 | **60.8** | **55.6** | |
| | ADELIE-DPO-3B | 39.2 | **47.8** | 60.7 | **55.6** | |
| |-----------------|-----------|---------|--------------|-----------------------| |
| | Qwen2.5 1.5B | 16.5 | 14.2 | 20.5 | 54.6 | |
| | ADELIE-SFT-1.5B | 37.7 | 44.6 | 58.9 | 55.0 | |
| | ADELIE-DPO-1.5B | **38.5** | **45.6** | **59.2** | **55.1** | |
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|
| ### Model Description |
|
|
| <!-- Provide a longer summary of what this model is. --> |
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| - **Developed by:** Yunjia Qi, Hao Peng, Xiaozhi Wang, Bin Xu, Lei Hou, Juanzi Li |
| - **Model type:** Text Generation |
| - **Language(s) (NLP):** English |
| - **License:** LLaMA2 License for the base model. |
| - **Finetuned from model [optional]:** LLaMA3.2-3B |
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