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Mohammed Ali

1 accepted papers

2025

How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models

EMNLP 2025

In this work, we present a systematic and comprehensive empirical evaluation of state-of-the-art reranking methods, encompassing large language model (LLM)-based, lightweight contextual, and zero-shot approaches, with respect to their performance in information retrieval tasks. We evaluate in total

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