Drowning in Documents: Consequences of Scaling Reranker Inference (2411.11767v1)

Abstract: Rerankers, typically cross-encoders, are often used to re-score the documents retrieved by cheaper initial IR systems. This is because, though expensive, rerankers are assumed to be more effective. We challenge this assumption by measuring reranker performance for full retrieval, not just re-scoring first-stage retrieval. Our experiments reveal a surprising trend: the best existing rerankers provide diminishing returns when scoring progressively more documents and actually degrade quality beyond a certain limit. In fact, in this setting, rerankers can frequently assign high scores to documents with no lexical or semantic overlap with the query. We hope that our findings will spur future research to improve reranking.

• The paper challenges the assumption that scaling reranker inference always enhances retrieval quality and instead shows degradation beyond a certain threshold.
• The paper employs a comprehensive evaluation using various retrievers such as BM25 and dense embeddings to highlight reranker limitations.
• The paper informs future design by emphasizing the need for robust reranker training with diversified negatives and holistic ranking strategies.

An Examination of Reranker Performance in Information Retrieval Systems

The field of information retrieval (IR) has witnessed significant advancements with the deployment of dual strategies: retrievers, typically embedding-based, and rerankers, often cross-encoders. This paper, "Drowning in Documents: Consequences of Scaling Reranker Inference," revisits the assumption ingrained in contemporary IR literature that rerankers invariably enhance retrieval quality when applied to an increasing number of documents, K. The authors challenge this postulate by empirically scrutinizing reranker behavior across a spectrum of public and enterprise IR benchmarks.

Key Findings

The crux of the paper lies in dismantling the belief that expanding K invariably improves rerankers' efficacy. Experimental outcomes suggest a counterintuitive trend: beyond a certain threshold of K, rerankers not only offer diminishing returns but may also degrade in quality. Specifically, rerankers could, at higher scales, erroneously assign high relevance scores to documents lacking lexical or semantic alignment with the query. This performance degradation underpins a fundamental discord between anticipated and actual reranker efficiency, especially when compared against standalone retrievers. The authors visually encapsulate these findings, emphasizing how rerankers' recall falters as K increases, contrasting with expected results.

Methodological Approach

The research meticulously applies rerankers against a diverse array of best-in-class retrievers, such as BM25 for lexical search and dense embedding models like OpenAI's text-embedding-3-large. The investigation involves various model pairings and reranking depths, offering a panoramic view of reranker dynamics vis-à-vis different models. This paper also extends reranking evaluations to entirety searches, scoring documents across full corpora—a scenario where rerankers, surprisingly, may underperform relative to efficient retrievers.

Implications for Reranking Design

The paper's revelations cast significant implications for the design of reranker models. The primary lesson is the potential inefficiency of current rerankers when tasked with extensive documentary scales. The unexpected errors in rerankers, like preference for irrelevant documents, illuminate deeper issues related to model robustness and possibly underspecified training particulars—indicating that rerankers' training might not expose them adequately to varied negative samples. Additionally, this research flags the risk of over-relying on reranker scaling as a proxy for enhancing IR quality.

Future Research Directions

Per the authors' aspirations, these findings steer the course toward refining reranker efficiency and robustness. Particularly promising is the prospect of leveraging listwise ranking strategies with LLMs, which demonstrated more reliable performance under similar conditions. Such models, employing a holistic approach over pointwise inference, could become pivotal in developing next-generation rerankers. Furthermore, rethinking how rerankers are trained—potentially integrating broader, more diversified negative samples—may address inherent training biases and inefficiencies, enhancing their scalability.

Conclusion

In summation, through rigorous analysis and cross-dataset experimentation, this paper establishes a nuanced perspective on reranking within IR systems. The findings urge a revaluation of current presumptions about scaling reranker inference, while inviting future innovations that could redefine their role and efficacy in information retrieval. For IR researchers, these insights propel the discourse towards achieving robust, efficient, and scalable retrieval solutions, reinforcing the necessity for continual reevaluation of theoretical assumptions against empirical realities.