While large language models (LLMs) have exhibited impressive instruction-following capabilities, it is still unclear whether and to what extent they can respond to explicit constraints that might be entailed in various instructions. As a significant aspect of LLM alignment, it is thus important to formulate such a specialized set of instructions as well as investigate the resulting behavior of LLMs. To address this vacancy, we propose a new benchmark CoDI-Eval to systematically and comprehensively evaluate LLMs' responses to instructions with various constraints. We construct a large collection of constraints-attributed instructions as a test suite focused on both generalization and coverage. Specifically, we advocate an instruction diversification process to synthesize diverse forms of constraint expression and also deliberate the candidate task taxonomy with even finer-grained sub-categories. Finally, we automate the entire evaluation process to facilitate further developments. Different from existing studies on controllable text generation, CoDI-Eval extends the scope to the prevalent instruction-following paradigm for the first time. We provide extensive evaluations of representative LLMs (e.g., ChatGPT, Vicuna) on CoDI-Eval, revealing their limitations in following instructions with specific constraints and there is still a significant gap between open-source and commercial closed-source LLMs. We believe this benchmark will facilitate research into improving the controllability of LLMs' responses to instructions. Our data and code are available at https://github.com/Xt-cyh/CoDI-Eval.

翻译：尽管大语言模型（LLMs）已展现出卓越的指令遵循能力，但目前仍不清楚它们能否以及能在多大程度上响应各类指令中可能蕴含的显式约束。作为大语言模型对齐的重要方面，制定此类专用指令集并研究LLMs的相应行为具有重要意义。为填补这一空白，我们提出新基准CoDI-Eval，用以系统全面评估LLMs对带有各种约束指令的响应能力。我们构建了一个大规模约束属性指令集作为测试套件，兼顾泛化性与覆盖率。具体而言，我们提出指令多样化流程以合成约束表达的不同变体，并精心设计了包含更细粒度子类别的候选任务分类体系。最终，我们实现全流程自动化评估以促进后续研究。与现有可控文本生成研究不同，CoDI-Eval首次将范畴扩展至主流的指令遵循范式。我们在CoDI-Eval上对代表性LLMs（如ChatGPT、Vicuna）进行了广泛评估，揭示了其在遵循特定约束指令方面的局限性，且开源与商业闭源LLMs之间仍存在显著差距。我们相信该基准将推动提升LLMs指令响应可控性的研究。相关数据与代码已开源至https://github.com/Xt-cyh/CoDI-Eval。