An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking (2411.05508v2)

Abstract: Recent advances have demonstrated that LLMs excel as listwise rerankers, but their high computational demands remain a barrier to widespread adoption. Further, the traditional LLMing (LM) objective is not ideally suited for reranking tasks. FIRST is a novel approach that addresses these challenges by integrating a learning-to-rank objective and leveraging the logits of only the first generated token, thereby significantly reducing inference latency compared to traditional LLM rerankers. In this study, we extend the evaluation of FIRST to the TREC Deep Learning datasets (DL19-22), validating its robustness across diverse domains. We investigate the influence of different first-stage retrievers on FIRST rerankers, observing diminishing returns and patterns consistent with traditional LLM rerankers. Through applying the FIRST objective to a broader range of backbone models, we achieve effectiveness surpassing the original implementation. Our experiments confirm that fast reranking with single-token logits does not compromise out-of-domain reranking quality. To better quantify the computational savings in the original study, we measure and compare latency to find a 21%-42% gain across various models and benchmarks. Moreover, while LM training implicitly improves zero-shot single-token reranking, our experiments also raise questions about whether LM pre-training may hinder subsequent fine-tuning with the FIRST objective. These findings pave the way for more efficient and effective listwise reranking in future applications.

An Insightful Overview of FIRST's Advancements in Fast Listwise Reranking

The paper "An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking" by Zijian Chen, Ronak Pradeep, and Jimmy Lin presents a paper on the advancement of efficiency in listwise reranking using LLMs. The central contribution of the paper lies in the introduction and evaluation of the FIRST approach, which combines a novel learning-to-rank objective with single-token decoding to improve the efficiency of LLM-based reranking systems without sacrificing effectiveness.

Summary and Contributions

The FIRST method introduces a reranking mechanism that focuses on the output logits of only the first generated token, significantly reducing computational latency traditionally associated with LLM rerankers. The paper evaluates this novel approach on the TREC Deep Learning datasets (DL19--22), thereby extending its analysis beyond previous experiments to validate its robustness across multiple domains.

Key contributions of the paper include:

• Validation Across Domains: Through experiments on TREC DL datasets, the authors confirmed the effectiveness and robustness of the FIRST approach.
• Latency Improvements: The paper reports a substantial reduction in inference latency, achieving improvements of between 21% and 42% compared to traditional LLM rerankers.
• Broadened Model Evaluation: The paper expanded the application of the FIRST objective to a wider range of backbone models, including Mistral and LLaMA, demonstrating improved effectiveness surpassing that of earlier implementations.
• Interaction with First-Stage Retrievers: Through examining various first-stage retrievers, FIRST's performance was consistent with established patterns observed in traditional LLM rerankers.

Methodological Advancements

The authors present a detailed breakdown of the FIRST methodology. A primary aspect is the use of a learning-to-rank loss that emphasizes the ranking of the most relevant documents within a list, diverging from the traditional LLMing loss which uniformly penalizes ranking errors. This shift highlights a crucial understanding that the objectives used in LLMs are not ideally suited for reranking tasks, which FIRST addresses with its tailored objective function.

Theoretical and Practical Implications

The implications of this paper are manifold. Practically, the reduction in computational demand makes FIRST an attractive choice for real-time information retrieval systems where latency is critical. The fact that these improvements do not compromise accuracy is significant as it suggests a viable pathway for deploying LLM rerankers more extensively in industry applications.

Theoretically, the paper sheds light on the intricate relationship between LLM pre-training and fine-tuning objectives. The counterintuitive finding that traditional LLMing may impede subsequent FIRST fine-tuning opens new avenues for investigation into learning-to-rank objectives that might harness the strengths of LLMs more effectively. It also suggests potential for optimizing pre-training strategies specifically geared towards reranking tasks.

Future Directions

The findings from the paper prompt further research into various aspects. A particularly interesting area would be exploring alternative ranking-specific objectives that might mitigate any hindrances posed by LLMing pre-training. Moreover, investigating the diminishing returns observed with different first-stage retriever qualities offers a potential basis for optimizing the balance between retrieval and reranking stages in multi-stage systems.

In conclusion, the paper on FIRST presents a critical step in refining listwise reranking methodologies. By achieving substantial gains in efficiency without sacrificing effectiveness, it paves the way for broader adoption and integration of LLM rerankers into practical applications, thereby marking a significant contribution to both the field of information retrieval and the broader landscape of LLM utilization.