用多个本地小模型组合，零样本提升葡萄牙语命名实体识别效果
Local LLM Ensembles for Zero-shot Portuguese Named Entity Recognition
- 构建三步集成流程，选最优模型组合提升性能
- 在5个数据集上4个超越单模型，跨数据集泛化强
- 无需标注数据即可部署，适合资源有限场景
大语言模型（LLMs）在上下文学习中表现优异，但在命名实体识别（NER）任务中，尤其对葡萄牙语等低资源语言表现不佳。尽管开源权重模型支持本地部署，但无单一模型能通用于所有任务，促使采用集成方法。然而，现有集成多聚焦文本生成或分类，未覆盖NER。本文提出一种新型三步集成流水线，利用可本地运行的同等级小模型实现零样本葡萄牙语NER。该方法在五个葡萄牙语NER数据集中的四个上优于单个模型，仅需极少标注数据即能通过启发式策略选择最优模型组合。此外，在不同源数据集上训练的集成模型在跨数据集设置中普遍优于单个模型，可能完全避免目标任务的标注需求。本工作通过有效组合多个小模型（无需微调），推进了可扩展、低资源、零样本的NER技术。代码已公开于 https://github.com/Joao-Luz/local-llm-ner-ensemble。
原文摘要 · Abstract (English)
Large Language Models (LLMs) excel in many Natural Language Processing (NLP) tasks through in-context learning but often under-perform in Named Entity Recognition (NER), especially for lower-resource languages like Portuguese. While open-weight LLMs enable local deployment, no single model dominates all tasks, motivating ensemble approaches. However, existing LLM ensembles focus on text generation or classification, leaving NER under-explored. In this context, this work proposes a novel three-step ensemble pipeline for zero-shot NER using similarly capable, locally run LLMs. Our method outperforms individual LLMs in four out of five Portuguese NER datasets by leveraging a heuristic to select optimal model combinations with minimal annotated data. Moreover, we show that ensembles obtained on different source datasets generally outperform individual LLMs in cross-dataset configurations, potentially eliminating the need for annotated data for the current task. Our work advances scalable, low-resource, and zero-shot NER by effectively combining multiple small LLMs without fine-tuning. Code is available at https://github.com/Joao-Luz/local-llm-ner-ensemble.
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