Yuqin Yang, Haowu Zhou, Haoran Tu, Zhiwen Hui, Shiqi Yan, HaoYang Li, Dong She, Xianrong Yao, Yang Gao, Zhanpeng Jin. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Recently, the direct application of large language models (LLMs) for in-context learning (ICL) in the field of information extraction (IE) has demonstrated impressive performance. However, ICL still lags behind the supervised fine-tuning approaches. Although current works attempt to apply ICL to IE tasks, they primarily emphasize positive ICL demonstrations without fully exploiting the potential value of negative instances. Incorporating negative instances introduces additional contextual variations and enhances the model’s sensitivity to subtle semantic differences. In this paper, we propose ReNIE, a semantic retrieval and negative instances augmented IE framework. Our framework introduces Semantic-Level Retrieval (SLR) and a Robustness Enhancement Strategy (RES) to enhance the model’s generation process. SLR retrieves demonstrations with the highest semantic similarity to serve as the model’s in-context prompt, guiding the model to generate predictions that align with task characteristics. RES comprises two components: the boundary disturbance method and the synonym perturbation method. The boundary disturbance method generates negative samples with boundary errors, guiding the model to avoid potential boundary-related mistakes, while the synonym perturbation method creates negative samples with synonym variations, providing additional contextual information to enhance the model’s understanding of predefined IE categories. To validate our proposed method, we conduct experiments on 13 Named Entity Recognition (NER) datasets, 2 Relation Extraction (RE) datasets, 2 Event Extraction (EE) datasets, and 3 Event Argument Extraction (EAE) datasets. Experimental results demonstrate that our method achieves comparable performance across the majority of datasets in unseen domains.