arXiv:2508.03865v4 Announce Type: replace Abstract: 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 veri
The rapid advancement of Large Language Models (LLMs) and the increasing demand for more accurate and context-aware AI systems are driving the development of specialized agents like this.
This development addresses a critical limitation in current Question Answering systems, enabling more reliable and precise information retrieval from knowledge bases by improving entity linking for ambiguous user queries.
Current QA systems will become more effective at understanding and responding to short, ambiguous queries by leveraging LLM-powered entity linking agents, leading to more robust AI applications.
- · AI developers
- · Knowledge base providers
- · Chatbot companies
- · Enterprise search solutions
- · Legacy entity linking solutions
- · Simple keyword search algorithms
More accurate and reliable AI-driven information retrieval for complex queries.
Increased adoption of AI in knowledge-intensive industries due to improved answer quality.
Accelerated development of fully autonomous AI agents capable of reasoning over vast knowledge stores.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL