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arXiv Computation and Language By Fina Polat, Daniel Daza, Pengyu Zhang, Klim Zaporojets, Paul Groth

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

Read the original on arXiv Computation and Language →

The paper investigates modular entity disambiguation by separating candidate retrieval from entity selection. It compares sparse retrieval (BM25), Web KB search, and a dense retriever, all paired with large language model selectors. Results show that a training‑free BM25 retriever combined with an LLM selector achieves state‑of‑the‑art performance on the ZELDA benchmark, and the modular approach enables abstention when retrieval fails.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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