How role-play shapes relevance judgment in zero-shot LLM rankers

• URL: http://arxiv.org/abs/2510.17535v1
• Date: Mon, 20 Oct 2025 13:39:48 GMT
• Title: How role-play shapes relevance judgment in zero-shot LLM rankers
• Authors: Yumeng Wang, Jirui Qi, Catherine Chen, Panagiotis Eustratiadis, Suzan Verberne,
• Abstract summary: Large Language Models (LLMs) have emerged as promising zero-shot rankers.<n>Their performance is highly sensitive to prompt formulation.<n>In particular, role-play prompts, where the model is assigned a functional role or identity, often give more robust and accurate relevance rankings.
• Score: 15.11127856890218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have emerged as promising zero-shot rankers, but their performance is highly sensitive to prompt formulation. In particular, role-play prompts, where the model is assigned a functional role or identity, often give more robust and accurate relevance rankings. However, the mechanisms and diversity of role-play effects remain underexplored, limiting both effective use and interpretability. In this work, we systematically examine how role-play variations influence zero-shot LLM rankers. We employ causal intervention techniques from mechanistic interpretability to trace how role-play information shapes relevance judgments in LLMs. Our analysis reveals that (1) careful formulation of role descriptions have a large effect on the ranking quality of the LLM; (2) role-play signals are predominantly encoded in early layers and communicate with task instructions in middle layers, while receiving limited interaction with query or document representations. Specifically, we identify a group of attention heads that encode information critical for role-conditioned relevance. These findings not only shed light on the inner workings of role-play in LLM ranking but also offer guidance for designing more effective prompts in IR and beyond, pointing toward broader opportunities for leveraging role-play in zero-shot applications.

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