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

Evaluating the Efficiency and Effectiveness of Cross-Encoders and LLM-Based Rerankers in Information Retrieval

Introduction

The landscape of Information Retrieval (IR) has been dramatically reshaped with the introduction of neural reranking methods, particularly with the advent of LLMs for task-specific applications. This study provides a comprehensive comparison between two dominant paradigms within the domain of neural reranking: cross-encoders and LLM-based rerankers, using SPLADE models as effective first-stage retrievers. Through extensive evaluation on in-domain (TREC Deep Learning datasets) and out-of-domain datasets (BEIR and LoTTE), this research illuminates the nuanced advantages and limitations of employing cross-encoders versus LLM-based methods for reranking, providing key insights into their operational efficiency and effectiveness across varied IR contexts.

Cross-Encoders and LLM-Based Rerankers: A Comparative Analysis

The Efficacy of Cross-Encoders

Cross-encoders, exemplified by models such as DeBERTa-v3 and ELECTRA, have been the cornerstone for reranking efforts in IR systems due to their ability to model interactions between query-document pairs effectively. These models, when coupled with efficient retrievers like SPLADE-v3, demonstrate substantial improvements in retrieval quality across both in-domain and out-of-domain datasets. However, their performance is monumentally influenced by the number of top documents reranked (top_k) and can be hindered by extensive computational requirements, making large-scale or real-time applications challenging.

LLMs as Rerankers: The GPT-3.5 Turbo and GPT-4 Phenomenon

LLMs, especially GPT-4, have shown a surprising capability in reranking tasks even in a zero-shot setting. The study indicates GPT-4's performance is competitive and, in certain scenarios, superior to traditional cross-encoders. Nonetheless, two significant caveats accompany the employment of GPT models for reranking: the prohibitive operational costs associated with using models like GPT-4 and the inefficiency induced by the model's constraint to manage large sets of documents for reranking. These factors pose substantial barriers to the practical deployment of LLMs in real-world IR systems.

The Implications and Future Directions

The nuanced analysis provides several critical insights for the deployment of neural rerankers in IR systems:

• Effectiveness and Efficiency Balance: While LLMs (particularly GPT-4) offer competitive or superior performance metrics, cross-encoders like DeBERTa-v3 provide a more balanced trade-off between effectiveness and operational efficiency.
• Resilience to Varied IR Contexts: The comparative efficacy of cross-encoders and LLM-based rerankers is context-dependent, with each exhibiting strengths in different IR scenarios—cross-encoders being more versatile across domains and LLMs showing exceptional prowess in specific contexts.
• Future of Reranking Pipelines: The analysis suggests the potential of combining cross-encoders and LLMs in cascading reranking pipelines to leverage the unique strengths of both approaches, pointing towards a hybrid future in neural reranking methodologies.

Conclusion

This study offers a granular investigation into the comparative merits of cross-encoders and LLM-based rerankers, framed by their application with the SPLADE models as first-stage retrievers. It presents a nuanced perspective that neither class of models universally outperforms the other across all IR tasks and settings. Instead, their deployment should be informed by a judicious assessment of the specific requirements and constraints of the application context, balancing the trade-offs between computational efficiency and retrieval effectiveness.