标题： LELA：一种基于大语言模型的实体链接方法，具有零样本领域适应能力 显示英文标题

标题： LELA: an LLM-based Entity Linking Approach with Zero-Shot Domain Adaptation

摘要： 实体链接（将文本中模糊的提及映射到知识库中的实体）是知识图谱构建、问答和信息提取等任务中的基础步骤。 我们的方法LELA是一种模块化的从粗到细的方法，利用了大语言模型（LLMs）的能力，并且可以在不同的目标领域、知识库和LLMs上工作，而无需任何微调阶段。 我们在各种实体链接设置中的实验表明，LELA与微调方法非常具有竞争力，并且显著优于非微调方法。 显示英文摘要

摘要： 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.