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