无需微调，LLM即可跨领域精准链接实体。
LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation
- 分步式粗到细框架，利用LLM能力自动识别实体。
- 零样本适配多领域，性能媲美微调模型。
- 适合快速部署于新场景，无需额外训练。
实体链接（将文本中模糊提及映射到知识库中的实体）是知识图谱构建、问答系统和信息抽取等任务的基础步骤。本文提出LELA，一种基于大语言模型的模块化粗到细实体链接方法，可在不同目标领域、知识库和大语言模型间通用，且无需任何微调阶段。在多种实体链接设置下的实验表明，LELA在性能上与微调方法相当，并显著优于非微调方法。
原文摘要 · Abstract (English)
Entity linking (mapping ambiguous mentions in text to entities in a knowledge base) is a foundational step in tasks such as knowledge graph construction, question-answering, and information extraction. Our method, LELA, is a modular coarse-to-fine approach that leverages the capabilities of large language models (LLMs), and works with different target domains, knowledge bases and LLMs, without any fine-tuning phase. Our experiments across various entity linking settings show that LELA is highly competitive with fine-tuned approaches, and substantially outperforms the non-fine-tuned ones.
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