Can LLMs Learn from Their Mistakes? Self-Correcting Instruction Tuning for Named Entity Recognition

AbstractRecent instruction-tuned large language models (LLMs) have demonstrated remarkable performance on various downstream tasks, including named entity recognition (NER). However, previous approaches often generate incorrect predictions, particularly regarding entity boundaries and types. Many of these errors can be corrected to match the ground truth by revising the entity boundaries and/or types. In this paper, we propose a self-correcting instruction tuning approach that simultaneously learns to perform NER and correct errors through natural language instructions. Self-correcting instruction tuning requires only a standard annotated NER dataset. Supervision for self-correction can be automatically generated from error patterns observed in LLMs fine-tuned solely on NER tasks. We conducted extensive experiments on eight NER datasets with two LLMs to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach enhances NER performance by effectively correcting prediction errors and substantially reducing false positives. We further analyze the self-correction behavior to better understand how the models improve performance.