ADELIE: Aligning Large Language Models on Information Extraction
This addresses the challenge of aligning LLMs for complex information extraction tasks, which is incremental as it adapts existing alignment methods to a specific domain.
The paper tackles the problem of large language models (LLMs) underperforming on information extraction (IE) tasks due to lack of alignment with human instructions, and introduces ADELIE, an aligned LLM that achieves state-of-the-art performance on various IE tasks among open-source models.
Large language models (LLMs) usually fall short on information extraction (IE) tasks and struggle to follow the complex instructions of IE tasks. This primarily arises from LLMs not being aligned with humans, as mainstream alignment datasets typically do not include IE data. In this paper, we introduce ADELIE (Aligning large language moDELs on Information Extraction), 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 IEInstruct for IE. Then we train ADELIE_SFT using instruction tuning on IEInstruct. 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. We will release the code, data, and models to facilitate further research.