← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.2175 · 记录 #1
CHOIR: Harmonizing Structured Persona Diversity for Robust Collaborative LLM Reasoning
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:46
摘要
Persona-assigned Large Language Models can adopt diverse roles, enabling personalized and context-aware reasoning. However, even minor demographic perturbations in personas, such as simple pronoun swaps, can alter reasoning trajectories, leading to divergent sets of correct answers on reasoning benchmarks. We explore the potential of these variations as a constructive resource to improve LLM reasoning performance. We propose CHOIR (Collaborative Harmonization fOr Inference Robustness), a test-time framework that harmonizes a set of demographically perturbed, persona-conditioned reasoning signals into a unified prediction. CHOIR orchestrates a collaborative decoding process among counterfactual personas perturbed across dimensions of gender, race, religion, disability, and age, dynamically balancing agreement and divergence in their reasoning paths to improve performance. Experiments demonstrate that CHOIR consistently enhances LLM reasoning across model architectures, scales, and tasks. Improvements reach up to 20.1% for individual groups and 15.1% on average, and we show that CHOIR remains effective even when base personas are suboptimal.
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记录 #1 · 2026-07 · 最新观测版本
CHOIR: Harmonizing Structured Persona Diversity for Robust Collaborative LLM Reasoning
Persona-assigned Large Language Models can adopt diverse roles, enabling personalized and context-aware reasoning. However, even minor demographic perturbations in personas, such as simple pronoun swaps, can alter reasoning trajectories, leading to divergent sets of correct answers on reasoning benchmarks. We explore the potential of these variations as a constructive resource to improve LLM reasoning performance. We propose CHOIR (Collaborative Harmonization fOr Inference Robustness), a test-time framework that harmonizes a set of demographically perturbed, persona-conditioned reasoning signals into a unified prediction. CHOIR orchestrates a collaborative decoding process among counterfactual personas perturbed across dimensions of gender, race, religion, disability, and age, dynamically balancing agreement and divergence in their reasoning paths to improve performance. Experiments demonstrate that CHOIR consistently enhances LLM reasoning across model architectures, scales, and tasks. Improvements reach up to 20.1% for individual groups and 15.1% on average, and we show that CHOIR remains effective even when base personas are suboptimal.
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