Ranked List Truncation for Large Language Model-based Re-Ranking
(2404.18185)Abstract
We study ranked list truncation (RLT) from a novel "retrieve-then-re-rank" perspective, where we optimize re-ranking by truncating the retrieved list (i.e., trim re-ranking candidates). RLT is crucial for re-ranking as it can improve re-ranking efficiency by sending variable-length candidate lists to a re-ranker on a per-query basis. It also has the potential to improve re-ranking effectiveness. Despite its importance, there is limited research into applying RLT methods to this new perspective. To address this research gap, we reproduce existing RLT methods in the context of re-ranking, especially newly emerged LLM-based re-ranking. In particular, we examine to what extent established findings on RLT for retrieval are generalizable to the "retrieve-then-re-rank" setup from three perspectives: (i) assessing RLT methods in the context of LLM-based re-ranking with lexical first-stage retrieval, (ii) investigating the impact of different types of first-stage retrievers on RLT methods, and (iii) investigating the impact of different types of re-rankers on RLT methods. We perform experiments on the TREC 2019 and 2020 deep learning tracks, investigating 8 RLT methods for pipelines involving 3 retrievers and 2 re-rankers. We reach new insights into RLT methods in the context of re-ranking.
Overview
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The paper evaluates the effectiveness of Ranked List Truncation (RLT) in LLM-based re-ranking systems, exploring optimal strategies for truncating ranked lists after initial document retrieval to improve search efficiencies.
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An extensive empirical study is conducted analyzing different retrievers' interaction with LLMs across TREC datasets, examining if traditional RLT approaches still hold in modern 'retrieve-then-re-rank' processes.
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It is observed that simpler RLT strategies, such as fixed cutoffs, often outperform more complex methods in balancing efficiency and effectiveness, suggesting a potential reevaluation of RLT in advanced retrieval systems.
Re-Evaluating Ranked List Truncation in LLM-based Re-Ranking
Introduction to Ranked List Truncation (RLT)
Ranked List Truncation (RLT) tackles how to determine the optimal number of items to re-rank after an initial retrieval stage. This concept is particularly crucial in improving search efficiencies, especially when dealing with computationally expensive re-rankers like LLMs. Traditionally applied in single-stage retrieval, RLT's applicability in a "retrieve-then-re-rank" context forms the core of this examination.
Empirical Study: LLM Re-Rankers with Various Retrievers
The researchers embarked on an expansive experimental journey involving three types of retrievers (lexical, learned sparse, and dense) cross-examined with LLM-based and traditional re-rankers across the TREC 2019 and 2020 datasets. They examined if traditional insights on RLT align with the needs and outcomes of modern retrieval-reranking pipelines.
Key Findings and Observations
Generalization Challenges:
- The expected benefits (efficiency and effectiveness) of applying RLT in re-ranking with LLMs didn't generalize well across setups. Using fixed cutoffs, often as shallow as the top 20 results, frequently matched or exceeded the efficiency and effectiveness of more sophisticated RLT methods.
Impact of Retriever Type:
- The type of initial retriever heavily influenced the efficacy of RLT in re-ranking. Specifically, retrievers like SPLADE++ or RepLLaMA that effectively prioritize relevant documents at the top of the list reduced the necessity for deep re-ranking, making simple cutoff strategies surprisingly effective.
Efficiency vs. Effectiveness:
- In scenarios where efficiency was prioritized, simple heuristics like fixed cutoffs often outperformed more complex, learned RLT methods in maintaining a balance between response quality and computational resource use.
Insights on RLT Methods:
- Among various RLT methodologies, distribution-based supervised approaches generally provided better trade-offs between re-ranking efficiency and effectiveness compared to their unsupervised counterparts and the sequential labeling strategies.
Theoretical and Practical Implications
The study critically underscores the nuanced application of RLT in complex "retrieve-then-re-rank" setups, especially with LLMs in the loop. Practically, it suggests that while advanced RLT methods may offer marginal gains in certain setups, the additional complexity and computational overhead may not justify their use over simpler heuristics like fixed-depth cutoffs. Theoretically, these findings invite a re-examination of how RLT principles apply in modern retrieval systems, possibly guiding future algorithmic adjustments or entirely new approaches to optimize both stages of retrieval and re-ranking.
Future Directions
Given the mixed results in efficacy across different setups and the critical role of the retriever's effectiveness, future research might focus on:
- Developing RLT strategies specifically tailored to the strengths and weaknesses of different types of retrievers and re-rankers.
- Exploring adaptive RLT methods that dynamically adjust re-ranking depth based on real-time assessments of query complexity and retriever performance.
- Extending these studies to other forms of re-rankers, such as pair-wise or list-wise models, to see how RLT might differently impact their efficiency and effectiveness trade-offs.
Conclusion
This examination of RLT in the context of LLM-based re-ranking illuminates the complexities and challenges of applying traditional IR (Information Retrieval) methodologies to advanced AI-driven systems. While there is no one-size-fits-all solution, the insights drawn from this extensive study provide a valuable roadmap for more nuanced, scenario-based applications of RLT in high-stakes retrieval tasks.
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