mlsanityData to 14 Oct 2026

STEER-BENCH: A Benchmark for Evaluating the Steerability of Large Language Models

arXiv 2505.20645 ↗First posted 27 May 2025cs.CLcited by 8

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Abstract (arXiv)

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 10,000 instruction-response pairs and validated 5,500 multiple-choice question 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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 30 of its 49 references that are in the map. First posted on arXiv 27 May 2025.

Builds on

30 of 49 references.

References · 30

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
  4. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  5. 2023WizardLM: Empowering large pre-trained language models to follow complex instructionscited by 1,125
  6. 2019DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphscited by 903
  7. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
  8. 2023Whose Opinions Do Language Models Reflect?cited by 773
  9. 2020Aligning AI With Shared Human Valuescited by 723
  10. 2023Character-LLM: A Trainable Agent for Role-Playingcited by 339
  11. 2023Evaluating Large Language Models at Evaluating Instruction Followingcited by 302
  12. 2024Personalized Steering of Large Language Models: Versatile Steering Vectors Through Bi-directional Preference Optimizationcited by 118
18 more
  1. 2023SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHFcited by 111
  2. 2024Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignmentcited by 104
  3. 2024CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologiescited by 98
  4. 2024Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Modelscited by 96
  5. 2025Steering Large Language Model Activations in Sparse Spacescited by 58
  6. 2023On the steerability of large language models toward data-driven personascited by 53
  7. 2023Steering Large Language Models for Machine Translation with Finetuning and In-Context Learningcited by 49
  8. 2024The Oscars of AI Theater: A Survey on Role-Playing with Language Modelscited by 48
  9. 2024Language Models for Text Classification: Is In-Context Learning Enough?cited by 40
  10. 2024CodecLM: Aligning Language Models with Tailored Synthetic Datacited by 39
  11. 2023Safer-Instruct: Aligning Language Models with Automated Preference Datacited by 27
  12. 2024How Susceptible are Large Language Models to Ideological Manipulation?cited by 26
  13. 2024Whose Emotions and Moral Sentiments Do Language Models Reflect?cited by 20
  14. 2024Is In-Context Learning Sufficient for Instruction Following in LLMs?cited by 19
  15. 2024SyntheT2C: Generating Synthetic Data for Fine-Tuning Large Language Models on the Text2Cypher Taskcited by 19
  16. 2024ComPO: Community Preferences for Language Model Personalizationcited by 19
  17. 2024Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Datacited by 17
  18. 2024COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communitiescited by 12

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 8.

8 papers.

  1. 2026Reflections and New Directions for Human-Centered Large Language Modelscited by 2
  2. 2026What Does the AI Doctor Value? Auditing Pluralism in the Clinical Ethics of Language Modelscited by 2
  3. 2026Steerability of Instrumental-Convergence Tendencies in LLMscited by 1
  4. 2026Mechanistic Indicators of Steering Effectiveness in Large Language Modelscited by 0
  5. 2026Not What, But How: A Framework for Auditing LLM Responses across Positioning, Generalization, Anthropomorphism, and Maximscited by 0
  6. 2026From Behavior to Mechanism: Tracing Divergent Response Modes in Frontier Language Modelscited by 0
  7. 2026MAxBench: A Multinomial Concept Recovery Benchmarkcited by 0
  8. 2026AgentPersonaBench: Benchmarking Persona-Driven User Simulationcited by 0