OBP-LLM: Optimizing Boundary Perception of Large Language Model for Few-shot NER
Abstract: Few-shot Named Entity Recognition (NER) enables models to learn effectively from limited annotated samples and perform robustly, even in resource-rich domains, addressing the challenge of scarce labeled data in many fields. Recently, Large Language Models (LLMs) have demonstrated strong adaptability and generalization capabilities in few-shot learning, offering new solutions for few-shot NER tasks. In this paper, we propose OBP-LLM, a novel approach that integrates attention-based contrastive learning and Direct Preference Optimization (DPO) to enhance the performance of large language models in few-shot tasks by optimizing the model’s perception of entity boundaries. Experimental results demonstrate that our method significantly outperforms existing approaches on multiple Few-shot NER benchmarks, including Few-NERD and CrossNER, particularly in cross-domain and extremely low-resource scenarios. This study validates the potential of contrastive learning and DPO in optimizing LLMs and provides new directions and practical solutions for NER tasks in low-resource domains.
Paper Type: Long
Research Area: Information Extraction
Research Area Keywords: Information Extraction,Information Retrieval and Text Mining
Languages Studied: Python
Submission Number: 6381
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