FIRST: Faster Improved Listwise Reranking with Single Token Decoding
(2406.15657)Abstract
LLMs have significantly advanced the field of information retrieval, particularly for reranking. Listwise LLM rerankers have showcased superior performance and generalizability compared to existing supervised approaches. However, conventional listwise LLM reranking methods lack efficiency as they provide ranking output in the form of a generated ordered sequence of candidate passage identifiers. Further, they are trained with the typical language modeling objective, which treats all ranking errors uniformly--potentially at the cost of misranking highly relevant passages. Addressing these limitations, we introduce FIRST, a novel listwise LLM reranking approach leveraging the output logits of the first generated identifier to directly obtain a ranked ordering of the candidates. Further, we incorporate a learning-to-rank loss during training, prioritizing ranking accuracy for the more relevant passages. Empirical results demonstrate that FIRST accelerates inference by 50% while maintaining a robust ranking performance with gains across the BEIR benchmark. Finally, to illustrate the practical effectiveness of listwise LLM rerankers, we investigate their application in providing relevance feedback for retrievers during inference. Our results show that LLM rerankers can provide a stronger distillation signal compared to cross-encoders, yielding substantial improvements in retriever recall after relevance feedback.
Overview
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The paper introduces 'FIRST,' an innovative approach that enhances the efficiency of listwise reranking in Information Retrieval (IR) by using LLMs and single-token decoding.
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Key contributions include the reduction of latency during the inference phase by leveraging the logits of the first generated identifier and integrating a learning-to-rank (LTR) loss to improve ranking performance.
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Empirical validation shows that FIRST outperforms existing methods in terms of ranking accuracy and reduces inference latency by 50%, demonstrating significant potential for practical applications in time-sensitive IR systems.
Insightful Overview of "FIRST: Faster Improved Listwise Reranking with Single Token Decoding"
The paper presents "FIRST," an innovative approach to improve the efficiency of listwise reranking in Information Retrieval (IR) using LLMs. The proposed method addresses the primary inefficiencies associated with conventional listwise LLM reranking, specifically the lengthy and computationally demanding process of generating an entire ordered sequence of candidate passage identifiers.
Motivation and Background
The prevailing trend in IR systems employs a multi-stage pipeline, where an initial set of candidates retrieved by an efficient algorithm is subsequently reranked by more sophisticated models to enhance relevance. LLMs have shown exceptional promise in this reranking step, particularly with listwise approaches that consider multiple passages in context to calibrate relevance scoring more effectively than pointwise or pairwise methods. However, the paper identifies key inefficiencies in the generation-based reranking approach, including the uniform treatment of ranking errors and the increased latency from generating full sequences of passage identifiers.
FIRST: A Single-Token Decoding Approach
The core contribution of the research is the introduction of FIRST, which achieves reranking by leveraging the output logits of the first generated identifier rather than generating a complete sequence of identifiers. This significantly reduces the latency associated with the inference phase. Furthermore, FIRST integrates a learning-to-rank (LTR) loss during training to prioritize the correct ranking of highly relevant passages, thereby improving the overall ranking performance.
Methodology
The methodology section explore the specifics of the FIRST approach:
- Single Token Decoding: FIRST ranks candidates based on the logits output for the identifier token of the first passage in the sequence. This method generates the ranking order directly from the initial logit values, eliminating the need for the sequence generation of passage IDs, thus speeding up the process.
- Learning-to-Rank Loss Incorporation: To overcome the limitations of traditional language modeling objectives, which uniformly penalize all ranking errors, FIRST incorporates a ranking loss based on RankNet. This loss function is augmented with an inverse mean rank weighting to give more importance to higher-ranking candidates.
Empirical results validate the efficacy of FIRST, demonstrating that it maintains high ranking performance while reducing the inference latency by 50%. This efficiency gain is particularly pronounced as the number of candidates per window increases.
Experimental Validation
The paper's experimental section demonstrates FIRST's performance on the BEIR benchmark, showing improvements in Normalized Discounted Cumulative Gain (nDCG) scores compared to existing methods such as RankZephyr and RankVicuna. Notably, FIRST outperforms these baselines despite being trained on a smaller dataset. Ablation studies reveal that the combination of the language modeling objective with the proposed RankNet loss outperforms either loss in isolation.
Latency and Practical Application
FIRST significantly reduces the time required for reranking by focusing on single-token decoding. This allows FIRST to process more candidate passages within the same time frame as traditional sequence generation approaches, leading to marked improvements in ranking effectiveness under latency constraints.
Additionally, the paper explores the practical implication of FIRST in relevance feedback mechanisms. The study shows that relevance feedback using FIRST leads to substantial improvements in recall for second-stage retrieval, outperforming traditional cross-encoder methods. This enhancement is attributed to the higher ranking accuracy of the LLM-based listwise reranker, highlighting its potential in real-world IR applications.
Implications and Future Developments
The research presents significant implications for the practical deployment of LLM-based rerankers in IR systems. By reducing inference latency without sacrificing performance, FIRST makes it feasible to employ complex listwise reranking methods in time-sensitive or resource-constrained settings. The integration of LTR losses further demonstrates the importance of aligning training objectives with the ultimate ranking goals, a practice that could be widely adopted in future LLM training protocols.
Future developments could explore the incorporation of human-annotated data alongside GPT-4 labeled examples to enhance the robustness and generalizability of the reranking model. Additionally, extending the approach to multilingual LLMs could broaden its applicability across different languages and domains.
Conclusion
FIRST represents a notable advancement in listwise reranking methodologies, illustrating that substantial gains in efficiency and effectiveness can be achieved through innovative modifications to both training and inference processes. By addressing the critical bottlenecks in current LLM reranking techniques, FIRST paves the way for more responsive and scalable IR systems using state-of-the-art language models.
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