用标签统计筛选关键词，让大模型更准地识别实体
LLMs are Better Than You Think: Label-Guided In-Context Learning for Named Entity Recognition
- 基于标签统计挑选最相关的示范样本，提升预测准确性
- 在5个数据集上超越现有方法，接近微调效果
- 适合资源少、需快速部署的实体识别场景
上下文学习（ICL）使大语言模型（LLMs）仅通过少量示例即可完成新任务。然而，在命名实体识别（NER）中，现有ICL方法通常依赖与任务无关的语义相似性来检索示例，常导致相关性不足，表现较差。本文提出DEER，一种无需训练的ICL方法，利用训练标签的词级统计信息，识别对实体识别最有帮助的标记，实现聚焦于实体的示范选择。该方法进一步利用这些统计信息，在针对性反思步骤中检测并修正易错标记。在四个LLM上的五个NER数据集上评估显示，DEER始终优于现有ICL方法，性能接近监督微调。进一步分析表明，DEER提升了示例检索质量，对已见和未见实体均有效，并在低资源环境下表现出强鲁棒性。
原文摘要 · Abstract (English)
In-context learning (ICL) enables large language models (LLMs) to perform new tasks using only a few demonstrations. However, in Named Entity Recognition (NER), existing ICL methods typically rely on task-agnostic semantic similarity for demonstration retrieval, which often yields less relevant examples and leads to inferior results. We introduce DEER, a training-free ICL approach that enables LLMs to make more informed entity predictions through the use of label-grounded statistics. DEER leverages token-level statistics from training labels to identify tokens most informative for entity recognition, enabling entity-focused demonstrations. It further uses these statistics to detect and refine error-prone tokens through a targeted reflection step. Evaluated on five NER datasets across four LLMs, DEER consistently outperforms existing ICL methods and achieves performance comparable to supervised fine-tuning. Further analyses demonstrate that DEER improves example retrieval, remains effective on both seen and unseen entities, and exhibits strong robustness in low-resource settings.
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