用大模型模拟人类思维，提升问答中短句实体链接准确率
An Entity Linking Agent for Question Answering
- 基于大模型构建认知式实体链接代理，主动识别与决策
- 在问答任务中表现优于传统方法，尤其在短而模糊的问题上
- 适合需要精准知识库映射的智能问答系统开发者
部分问答系统依赖知识库（KB）提供准确答案。实体链接（EL）在将自然语言提及映射到知识库条目中起关键作用。然而，大多数现有EL方法针对长文本设计，在问答任务中短且模糊的用户问题上表现不佳。本文提出一种面向问答的实体链接代理，基于大语言模型模拟人类认知流程：主动识别实体提及、检索候选实体并作出判断。为验证其有效性，我们开展两项实验：基于工具的实体链接与问答任务评估。结果证实该代理具有强鲁棒性与有效性。
原文摘要 · Abstract (English)
Some Question Answering (QA) systems rely on knowledge bases (KBs) to provide accurate answers. Entity Linking (EL) plays a critical role in linking natural language mentions to KB entries. However, most existing EL methods are designed for long contexts and do not perform well on short, ambiguous user questions in QA tasks. We propose an entity linking agent for QA, based on a Large Language Model that simulates human cognitive workflows. The agent actively identifies entity mentions, retrieves candidate entities, and makes decision. To verify the effectiveness of our agent, we conduct two experiments: tool-based entity linking and QA task evaluation. The results confirm the robustness and effectiveness of our agent.
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