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.

使用 ADELIE 模型与高质量对齐数据集 IEInstruct 进行指令调整和直接优化，实现了信息抽取任务的最新性能，并探索了 ADELIE 的通用能力。