mlsanityData to 14 Oct 2026

An Entity Linking Agent for Question Answering

arXiv 2508.03865 ↗First posted 5 Aug 2025cs.CLcited by 2

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 47 of its 65 references that are in the map. First posted on arXiv 5 Aug 2025.

Builds on

47 of 65 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning20 May

Other references · 46

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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34 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Stop Wasting Your Tokens: Towards Efficient Runtime Multi-Agent Systemscited by 12
  2. 2026Think Before You Link: Rarity, Reasoning, and Retrieval in Multilingual Entity Linkingcited by 0

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  8. 2025A Survey of Large Language Model Agents for Question Answering