Go to ACL ARR 2025 May homepageSTEER-BENCH: A Benchmark for Evaluating the Steerability of Large Language Models
Abstract: Steerability, or the ability of large language models (LLMs) to adapt outputs to align with diverse community-specific norms, perspectives, and communication styles, is critical for real-world applications but remains under-evaluated. We introduce STEER-BENCH, a benchmark for assessing population-specific steering using contrasting Reddit communities. Covering 30 contrasting subreddit pairs across 19 domains, STEER-BENCH includes over 5,000 instruction-response pairs and validated 5,500 multiple-choice questions with corresponding silver labels to test alignment with diverse community norms. Our evaluation of 13 popular LLMs using STEER-BENCH reveals that while human experts achieve an accuracy of 81% with silver labels, the best-performing models reach only around 65% accuracy depending on the domain and configuration. Some models lag behind human-level alignment by over 15 percentage points, highlighting significant gaps in community-sensitive steerability. STEER-BENCH is a benchmark to systematically assess how effectively LLMs understand community-specific instructions, their resilience to adversarial steering attempts, and their ability to accurately represent diverse cultural and ideological perspectives.
Paper Type: Long
Research Area: Computational Social Science and Cultural Analytics
Research Area Keywords: Large Lanuage Model, Evaluation,Steerability,Benchmark
Contribution Types: Data resources
Languages Studied: English
Submission Number: 2454
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