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How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities

arXiv – CS AI|Ziwen Xu, Kewei Xu, Haoming Xu, Haiwen Hong, Longtao Huang, Hui Xue, Ningyu Zhang, Yongliang Shen, Guozhou Zheng, Huajun Chen, Shumin Deng|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce SteerEval, a new benchmark for evaluating how controllable Large Language Models are across language features, sentiment, and personality domains. The study reveals that current steering methods often fail at finer-grained control levels, highlighting significant risks when deploying LLMs in socially sensitive applications.

Key Takeaways

→SteerEval provides a hierarchical framework to test LLM controllability across three behavioral domains with three specification levels each.
→Current steering methods show degraded performance when attempting fine-grained control of LLM behavior.
→LLMs deployed in socially sensitive domains face risks from unpredictable behaviors including misaligned intent and inconsistent personality.
→The benchmark connects high-level behavioral intent to concrete textual output for more principled evaluation.
→This research establishes a foundation for developing safer and more controllable AI systems.

#llm #ai-safety #controllability #benchmark #steering #evaluation #behavioral-ai #research #steereval

Read Original →via arXiv – CS AI

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