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Fina Polat

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#artificial intelligence Preprint Aug 2026

Select, Don't Train: The Benefits of Modular Entity Disambiguation with LLM-Based Selection

A systematic comparison of retrieval strategies for candidate generation under a shared LLM-based selection stage, combining sparse retrieval (BM25), Web KB search, and a state-of-the-art trained dense retriever with several open- and closed-source LLMs is presented.

Fina Polat, Daniel Daza, Pengyu Zhang et al. · 0 citations