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arXiv · 2403.10407

A Thorough Comparison of Cross-Encoders and LLMs for Reranking SPLADE

Hervé Déjean·Stéphane Clinchant·Thibault Formal
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Abstract

We present a comparative study between cross-encoder and LLMs rerankers in the context of re-ranking effective SPLADE retrievers. We conduct a large evaluation on TREC Deep Learning datasets and out-of-domain datasets such as BEIR and LoTTE. In the first set of experiments, we show how cross-encoder rerankers are hard to distinguish when it comes to re-rerank SPLADE on MS MARCO. Observations shift in the out-of-domain scenario, where both the type of model and the number of documents to re-rank have an impact on effectiveness. Then, we focus on listwise rerankers based on Large Language Models -- especially GPT-4. While GPT-4 demonstrates impressive (zero-shot) performance, we show that traditional cross-encoders remain very competitive. Overall, our findings aim to to provide a more nuanced perspective on the recent excitement surrounding LLM-based re-rankers -- by positioning them as another factor to consider in balancing effectiveness and efficiency in search systems.

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Hervé Déjean, Stéphane Clinchant, Thibault Formal. 2024-03-15. A Thorough Comparison of Cross-Encoders and LLMs for Reranking SPLADE. https://arxiv.org/abs/2403.10407

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