An Investigation of Prompt Variations for Zero-shot LLM-based Rankers

2024-06-20

arXiv.org

AI Summary

This study investigates the extent to which prompt design influences zero-shot large language model (LLM) ranking performance—and whether its impact surpasses that of ranking algorithm selection and underlying model architecture (e.g., GPT-3.5, FLAN-T5). Through large-scale, controlled ablation experiments, we systematically disentangle the effects of prompt components (e.g., role specification phrasing), ranking paradigms (pairwise vs. listwise), and model architecture. Our key finding—quantified for the first time—is that fine-grained prompt design significantly outweighs algorithmic differences across multiple evaluation scenarios; moreover, algorithmic advantages substantially diminish under prompt perturbations. This work challenges the prevailing consensus that attributes ranking performance primarily to algorithm or model choice, and establishes a new, reproducible, and attribution-aware benchmark for LLM-based ranking research.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Natural Language Processing: Prompt Engineering / Prompting
• Search and Optimization: Algorithm Configuration

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We provide a systematic understanding of the impact of specific components and wordings used in prompts on the effectiveness of rankers based on zero-shot Large Language Models (LLMs). Several zero-shot ranking methods based on LLMs have recently been proposed. Among many aspects, methods differ across (1) the ranking algorithm they implement, e.g., pointwise vs. listwise, (2) the backbone LLMs used, e.g., GPT3.5 vs. FLAN-T5, (3) the components and wording used in prompts, e.g., the use or not of role-definition (role-playing) and the actual words used to express this. It is currently unclear whether performance differences are due to the underlying ranking algorithm, or because of spurious factors such as better choice of words used in prompts. This confusion risks to undermine future research. Through our large-scale experimentation and analysis, we find that ranking algorithms do contribute to differences between methods for zero-shot LLM ranking. However, so do the LLM backbones -- but even more importantly, the choice of prompt components and wordings affect the ranking. In fact, in our experiments, we find that, at times, these latter elements have more impact on the ranker's effectiveness than the actual ranking algorithms, and that differences among ranking methods become more blurred when prompt variations are considered.

Problem

• LLM-based Models
• Prompt Influence
• Game Ranking

Innovation

• LLMs Ranking Games
• Prompt Engineering
• Game Outcome Influence