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

TL;DR

This paper systematically evaluates state-of-the-art reranking models, including LLM-based and lightweight approaches, across multiple benchmarks to understand their performance on familiar and novel queries in information retrieval.

Contribution

It provides a comprehensive empirical comparison of 22 reranking methods, analyzing factors like training data overlap, architecture, and efficiency, revealing insights into their generalization capabilities.

Findings

LLM-based rerankers outperform on familiar queries

Lightweight models are more efficient and comparable on some tasks

Query novelty significantly affects reranking effectiveness

Abstract

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 22 methods, including 40 variants (depending on used LLM) across several established benchmarks, including TREC DL19, DL20, and BEIR, as well as a novel dataset designed to test queries unseen by pretrained models. Our primary goal is to determine, through controlled and fair comparisons, whether a performance disparity exists between LLM-based rerankers and their lightweight counterparts, particularly on novel queries, and to elucidate the underlying causes of any observed differences. To disentangle confounding factors, we analyze the effects of training data overlap, model architecture, and computational efficiency on reranking performance. Our findings indicate that while LLM-based rerankers demonstrate superior performance on familiar queries, their generalization ability to novel queries varies, with lightweight models offering comparable efficiency. We further identify that the novelty of queries significantly impacts reranking effectiveness, highlighting limitations in existing approaches. https://github.com/DataScienceUIBK/llm-reranking-generalization-study