FIRST: Faster Improved Listwise Reranking with Single Token Decoding
FIRST method improves reranking efficiency by 50% using single token decoding on the BEIR benchmark.
Key Findings
Methodology
The FIRST method leverages the output logits of the first generated identifier to directly obtain a ranked ordering of candidates. It incorporates a learning-to-rank loss during training, prioritizing ranking accuracy for more relevant passages. This approach significantly improves reranking efficiency through single token decoding.
Key Results
- FIRST accelerates inference by 50% on the BEIR benchmark while maintaining robust ranking performance.
- Compared to RankZephyr, FIRST outperforms despite being fine-tuned on less data.
- Experiments show that FIRST significantly improves retriever recall when providing relevance feedback.
Significance
The FIRST method holds significant value in both academia and industry. It addresses the inefficiency of traditional listwise LLM reranking methods and demonstrates superior performance in providing relevance feedback. This research offers new insights for the information retrieval field, especially in applications requiring rapid response.
Technical Contribution
The FIRST method fundamentally differs from existing approaches by achieving reranking through single token decoding, significantly reducing inference latency. It introduces a learning-to-rank loss that enhances the ability to rank highly relevant candidates and demonstrates superior performance across various datasets.
Novelty
FIRST is the first method to employ single token decoding for listwise reranking, significantly improving inference efficiency. Its innovation lies in using the logits of the first generated identifier for ranking instead of generating the entire sequence.
Limitations
- The FIRST method may face challenges when handling very large candidate sets, as the effectiveness of single token decoding might diminish.
- Further research is needed to maintain efficiency across different domains.
Future Work
Future research could explore the application of the FIRST method on larger datasets and its integration with other optimization strategies. Additionally, studying how to maintain its efficiency across different domains is an important direction.
AI Executive Summary
In the field of information retrieval, traditional listwise reranking methods are inefficient, especially in scenarios requiring rapid response. The FIRST method significantly improves reranking efficiency through single token decoding, addressing this issue.
The core of the FIRST method lies in leveraging the output logits of the first generated identifier to directly obtain a ranked ordering of candidates. By incorporating a learning-to-rank loss, FIRST prioritizes ranking accuracy for more relevant passages. This innovation allows FIRST to accelerate inference by 50% on the BEIR benchmark while maintaining robust ranking performance.
Experimental results show that FIRST significantly improves retriever recall when providing relevance feedback. The method holds significant value in both academia and industry, offering new insights for the information retrieval field. Future research could explore the application of the FIRST method on larger datasets and its integration with other optimization strategies.
Deep Analysis
Background
The field of information retrieval has seen significant advancements recently, particularly with the application of large language models. Traditional reranking methods often rely on supervised learning, which is inefficient and struggles to meet the demands of rapid response. Recently, listwise reranking methods have gained attention for their ability to score multiple candidates simultaneously.
Core Problem
Traditional listwise reranking methods are inefficient, particularly in scenarios requiring rapid response. The process of generating entire candidate sequences is time-consuming and unnecessary, leading to increased inference latency.
Innovation
The FIRST method fundamentally differs from existing approaches by achieving reranking through single token decoding. It leverages the output logits of the first generated identifier to directly obtain a ranked ordering of candidates, significantly improving inference efficiency. The introduction of a learning-to-rank loss enhances the ability to rank highly relevant candidates.
Methodology
- �� Leverage the output logits of the first generated identifier to directly obtain a ranked ordering of candidates.
- �� Incorporate a learning-to-rank loss, prioritizing ranking accuracy for more relevant passages.
- �� Achieve significant improvements in reranking efficiency through single token decoding.
Experiments
Experiments were conducted on the BEIR benchmark using Contriever as the initial retriever. Listwise reranking was performed using a sliding window strategy with a window size of 20 and a step size of 10. Results show that FIRST outperforms existing methods in both inference speed and ranking performance.
Results
FIRST accelerates inference by 50% on the BEIR benchmark while maintaining robust ranking performance. Compared to RankZephyr, FIRST outperforms despite being fine-tuned on less data. Experiments show that FIRST significantly improves retriever recall when providing relevance feedback.
Applications
The FIRST method is applicable in scenarios requiring rapid response, such as real-time search and online recommendation systems. Its efficiency and accuracy make it highly applicable in the industry.
Limitations & Outlook
The FIRST method may face challenges when handling very large candidate sets, as the effectiveness of single token decoding might diminish. Further research is needed to maintain efficiency across different domains.
Plain Language Accessible to non-experts
Imagine you're in a library looking for a book. Traditional methods are like having the librarian arrange all the books in order for you, which takes time. The FIRST method is like having the librarian just tell you where the most relevant book is, saving a lot of time. This method quickly identifies the most relevant books, greatly improving efficiency.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to quickly find hidden treasure. Traditional methods are like opening each box one by one, which is time-consuming and tiring. The FIRST method is like having a super helper who can instantly tell you which box has the treasure! Isn't that cool? That's the power of the FIRST method, helping you find what you want quickly!
Glossary
Logits
In machine learning, logits are the unnormalized probability values output by a model.
Used in the FIRST method for ranking candidates.
Learning to Rank
A machine learning method used to order items based on relevance.
Used in FIRST to improve ranking accuracy.
BEIR Benchmark
A standard dataset used to evaluate the performance of information retrieval systems.
FIRST's performance was tested on this benchmark.
Single Token Decoding
A decoding technique that infers the entire sequence by generating a single token.
Core technology of the FIRST method.
Cross-Encoder
A model used for text matching by encoding the query and candidate together to compute relevance.
Compared with FIRST for performance.
Open Questions Unanswered questions from this research
- 1 How to maintain the efficiency of the FIRST method across different domains remains to be studied.
- 2 The performance of FIRST on very large-scale datasets has not been fully verified.
Applications
Immediate Applications
Real-time Search
The FIRST method can be used to improve the response speed and accuracy of real-time search systems.
Long-term Vision
Online Recommendation Systems
The efficiency of the FIRST method makes it widely applicable in online recommendation systems.
Abstract
Large Language Models (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.