ADELIE-DPO-1.5B

Model Card for ADELIE-DPO-1.5B

<!-- 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_SFT using instruction tuning on <font face="Verdana">IEInstruct</font>. We further train ADELIE_SFT with direct preference optimization (DPO) objective, resulting in ADELIE_DPO. Extensive experiments on various held-out IE datasets demonstrate that our models (ADELIE_SFT and ADELIE_DPO) 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.

• 📖 Paper: ADELIE: Aligning Large Language Models on Information Extraction

</p> - 🐧 Github: [THU/ADELIE](https://github.com/THU-KEG/ADELIE/tree/main)

Model Performance

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 Description

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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]: Qwen2.5-1.5B