BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition
This work addresses challenges in few-shot NER for natural language processing applications, representing an incremental improvement with novel method components.
The paper tackles the problem of false spans and unaligned prototypes in few-shot named entity recognition by proposing a boundary-aware contrastive learning strategy and using LoRAHub for cross-domain alignment, resulting in outperformance over prior methods across various benchmarks.
Despite the recent success of two-stage prototypical networks in few-shot named entity recognition (NER), challenges such as over/under-detected false spans in the span detection stage and unaligned entity prototypes in the type classification stage persist. Additionally, LLMs have not proven to be effective few-shot information extractors in general. In this paper, we propose an approach called Boundary-Aware LLMs for Few-Shot Named Entity Recognition to address these issues. We introduce a boundary-aware contrastive learning strategy to enhance the LLM's ability to perceive entity boundaries for generalized entity spans. Additionally, we utilize LoRAHub to align information from the target domain to the source domain, thereby enhancing adaptive cross-domain classification capabilities. Extensive experiments across various benchmarks demonstrate that our framework outperforms prior methods, validating its effectiveness. In particular, the proposed strategies demonstrate effectiveness across a range of LLM architectures. The code and data are released on https://github.com/UESTC-GQJ/BANER.