How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities
arXiv:2603.02578v1 Announce Type: new Abstract: 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.
arXiv:2603.02578v1 Announce Type: new Abstract: 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.
Executive Summary
The article introduces SteerEval, a hierarchical benchmark for evaluating the controllability of Large Language Models (LLMs) across three domains: language features, sentiment, and personality. The benchmark assesses LLMs at three specification levels, connecting high-level intent to concrete output. The evaluation reveals that control degrades at finer-grained levels, highlighting the need for a principled framework for safe and controllable LLM behavior. This research provides a foundation for future studies on LLM controllability, emphasizing the importance of developing methods that can effectively steer LLMs across various domains and levels of granularity.
Key Points
- ▸ Introduction of SteerEval, a hierarchical benchmark for evaluating LLM controllability
- ▸ Assessment of LLMs across three domains: language features, sentiment, and personality
- ▸ Evaluation of LLMs at three specification levels: L1, L2, and L3
Merits
Comprehensive Framework
SteerEval provides a principled and interpretable framework for evaluating LLM controllability, allowing for a systematic assessment of LLMs across various domains and levels of granularity.
Demerits
Limited Generalizability
The study's findings may not be generalizable to all LLMs or domains, as the evaluation is limited to contemporary steering methods and a specific set of LLMs.
Expert Commentary
The introduction of SteerEval marks a significant step forward in the development of controllable LLMs. By providing a comprehensive framework for evaluating LLM controllability, this research highlights the need for a more nuanced understanding of LLM behavior and the importance of developing methods that can effectively steer LLMs across various domains and levels of granularity. As LLMs become increasingly ubiquitous in socially sensitive domains, the development of controllable LLMs is crucial for ensuring AI safety and ethics. Further research is needed to build upon the findings of this study and to develop more effective steering methods for LLMs.
Recommendations
- ✓ Future research should focus on developing more effective steering methods for LLMs, allowing for finer-grained control over language features, sentiment, and personality.
- ✓ The development of regulatory frameworks and guidelines for the development and deployment of LLMs should prioritize controllability and safety in AI systems.