RoleConflictBench: A Benchmark of Role Conflict Scenarios for Evaluating LLMs' Contextual Sensitivity

2025-09-30 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

RoleConflictBench is a new benchmark designed to evaluate the contextual sensitivity of large language models (LLMs) in social dilemmas involving conflicting role expectations. This benchmark addresses the question of whether LLMs prioritize dynamic contextual cues or learned preferences when faced with such scenarios. It employs situational urgency as an objective constraint for decision-making within this inherently subjective domain. The dataset, comprising over 13,000 realistic scenarios, was constructed through a three-stage pipeline, covering 65 roles across five social domains by systematically varying the urgency of competing situations. An analysis of 10 LLMs using RoleConflictBench revealed that these models substantially deviate from an objective baseline, with their decisions predominantly governed by preferences toward specific social roles rather than dynamic contextual cues.

Key takeaway

For research scientists developing or deploying LLMs in socially sensitive applications, you should rigorously test your models using benchmarks like RoleConflictBench. This will help you identify and mitigate the observed tendency for LLMs to prioritize static, learned role preferences over dynamic contextual cues, which could lead to inappropriate or biased responses in real-world role conflict scenarios.

Key insights

LLMs prioritize learned role preferences over dynamic contextual cues in social role conflict scenarios.

Principles

• Situational urgency can objectively constrain subjective decision-making.
• LLMs exhibit a bias towards static role preferences.

Method

RoleConflictBench uses a three-stage pipeline to generate 13,000+ scenarios across 65 roles, systematically varying situational urgency to measure LLM contextual sensitivity.

In practice

• Evaluate LLMs for social dilemma handling.
• Test model bias towards specific roles.

Topics

• Role Conflict Scenarios
• LLM Contextual Sensitivity
• RoleConflictBench Benchmark
• Social Dilemmas
• LLM Evaluation

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.