Author

Daniel Daza

View on Semantic Scholar

1 paper indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#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