How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities
Authors: name: Ziwen Xu, name: Kewei Xu, name: Haoming Xu, name: Haiwen Hong, name: Longtao Huang, name: Hui Xue, name: Ningyu Zhang, name: Yongliang Shen, name: Guozhou Zheng, name: Huajun Chen, name: Shumin Deng
Organization: AGI Lab

Large Language Models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors, ranging from misaligned intent to inconsistent personality, pose significant risks. We introduce SteerEval, a hierarchical benchmark for evaluating LLM controllability across three domains: language features, sentiment, and personality. Each domain is structured into three specification levels: L1 (what to express), L2 (how to express), and L3 (how to instantiate), connecting high-level behavioral intent to concrete textual output. Using SteerEval, we systematically evaluate contemporary steering methods, revealing that control often degrades at finer-grained levels. Our benchmark offers a principled and interpretable framework for safe and controllable LLM behavior, serving as a foundation for future research.