Beyond Reproducibility: Advancing Zero-shot LLM Reranking Efficiency with Setwise Insertion
- URL: http://arxiv.org/abs/2504.10509v1
- Date: Wed, 09 Apr 2025 18:44:34 GMT
- Title: Beyond Reproducibility: Advancing Zero-shot LLM Reranking Efficiency with Setwise Insertion
- Authors: Jakub Podolak, Leon Peric, Mina Janicijevic, Roxana Petcu,
- Abstract summary: This study presents a comprehensive and extension analysis of the Setwise prompting methodology for zero-shot ranking with Large Language Models (LLMs)<n>We evaluate its effectiveness and efficiency compared to traditional Pointwise, Pairwise, and Listwise approaches in document ranking tasks.<n>We introduce Setwise Insertion, a novel approach that leverages the initial document ranking as prior knowledge.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study presents a comprehensive reproducibility and extension analysis of the Setwise prompting methodology for zero-shot ranking with Large Language Models (LLMs), as proposed by Zhuang et al. We evaluate its effectiveness and efficiency compared to traditional Pointwise, Pairwise, and Listwise approaches in document ranking tasks. Our reproduction confirms the findings of Zhuang et al., highlighting the trade-offs between computational efficiency and ranking effectiveness in Setwise methods. Building on these insights, we introduce Setwise Insertion, a novel approach that leverages the initial document ranking as prior knowledge, reducing unnecessary comparisons and uncertainty by focusing on candidates more likely to improve the ranking results. Experimental results across multiple LLM architectures (Flan-T5, Vicuna, and Llama) show that Setwise Insertion yields a 31% reduction in query time, a 23% reduction in model inferences, and a slight improvement in reranking effectiveness compared to the original Setwise method. These findings highlight the practical advantage of incorporating prior ranking knowledge into Setwise prompting for efficient and accurate zero-shot document reranking.
Related papers
- An Early FIRST Reproduction and Improvements to Single-Token Decoding for Fast Listwise Reranking [50.81324768683995]
FIRST is a novel approach that integrates a learning-to-rank objective and leveraging the logits of only the first generated token.
We extend the evaluation of FIRST to the TREC Deep Learning datasets (DL19-22), validating its robustness across diverse domains.
Our experiments confirm that fast reranking with single-token logits does not compromise out-of-domain reranking quality.
arXiv Detail & Related papers (2024-11-08T12:08:17Z) - Self-Calibrated Listwise Reranking with Large Language Models [137.6557607279876]
Large language models (LLMs) have been employed in reranking tasks through a sequence-to-sequence approach.
This reranking paradigm requires a sliding window strategy to iteratively handle larger candidate sets.
We propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking.
arXiv Detail & Related papers (2024-11-07T10:31:31Z) - Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation [6.979979613916754]
News recommendation is a challenging task that involves personalization based on the interaction history and preferences of each user.
Recent works have leveraged the power of pretrained language models (PLMs) to directly rank news items by using inference approaches that predominately fall into three categories: pointwise, pairwise, and listwise learning-to-rank.
We propose a novel framework for PLM-based news recommendation that integrates both pointwise relevance prediction and pairwise comparisons in a scalable manner.
arXiv Detail & Related papers (2024-09-26T10:27:19Z) - AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning [50.78033979438031]
We first introduce a unified formulation to analyze CLIP-based few-shot learning methods from a perspective of logit bias.
Based on analysis of key components, this paper proposes a novel AMU-Tuning method to learn effective logit bias for CLIP-based few-shot classification.
arXiv Detail & Related papers (2024-04-13T10:46:11Z) - Lower-Left Partial AUC: An Effective and Efficient Optimization Metric
for Recommendation [52.45394284415614]
We propose a new optimization metric, Lower-Left Partial AUC (LLPAUC), which is computationally efficient like AUC but strongly correlates with Top-K ranking metrics.
LLPAUC considers only the partial area under the ROC curve in the Lower-Left corner to push the optimization focus on Top-K.
arXiv Detail & Related papers (2024-02-29T13:58:33Z) - Instruction Distillation Makes Large Language Models Efficient Zero-shot
Rankers [56.12593882838412]
We introduce a novel instruction distillation method to rank documents.
We first rank documents using the effective pairwise approach with complex instructions, and then distill the teacher predictions to the pointwise approach with simpler instructions.
Our approach surpasses the performance of existing supervised methods like monoT5 and is on par with the state-of-the-art zero-shot methods.
arXiv Detail & Related papers (2023-11-02T19:16:21Z) - A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models [35.17291316942284]
We propose a novel zero-shot document ranking approach based on Large Language Models (LLMs): the Setwise prompting approach.
Our approach complements existing prompting approaches for LLM-based zero-shot ranking: Pointwise, Pairwise, and Listwise.
arXiv Detail & Related papers (2023-10-14T05:20:02Z) - Zero-Shot Listwise Document Reranking with a Large Language Model [58.64141622176841]
We propose Listwise Reranker with a Large Language Model (LRL), which achieves strong reranking effectiveness without using any task-specific training data.
Experiments on three TREC web search datasets demonstrate that LRL not only outperforms zero-shot pointwise methods when reranking first-stage retrieval results, but can also act as a final-stage reranker.
arXiv Detail & Related papers (2023-05-03T14:45:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.