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--- |
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license: llama2 |
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datasets: |
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- ACE05 |
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- conll2003 |
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- conll2012_ontonotesv5 |
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- rams |
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- tacred |
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- fewrel |
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- maven |
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language: |
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- en |
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metrics: |
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- f1 |
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pipeline_tag: text-generation |
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tags: |
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- text-generation-inference |
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- Information Extraction |
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- IE |
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- Named Entity Recogniton |
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- Event Extraction |
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- Relation Extraction |
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- LLaMA |
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--- |
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# Model Card for ADELIE-SFT |
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<!-- Provide a quick summary of what the model is/does. --> |
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<p align="justify"> |
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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) |
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</p> |
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- 🐧 Github: [THU/ADELIE](https://github.com/THU-KEG/ADELIE/tree/main) |
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### Model Description |
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<!-- 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 |
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- **Model type:** Text Generation |
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- **Language(s) (NLP):** English |
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- **License:** LLaMA2 License for the base model. |
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- **Finetuned from model [optional]:** LLaMA2-7B |
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