Although Large Language Models (LLMs) have demonstrated strong instruction-following ability to be helpful, they are further supposed to be controlled and guided by rules in real-world scenarios to be safe, and accurate in responses. This demands the possession of rule-following capability of LLMs. However, few works have made a clear evaluation of the rule-following capability of LLMs. Previous studies that try to evaluate the rule-following capability of LLMs fail to distinguish the rule-following scenarios from the instruction-following scenarios. Therefore, this paper first makes a clarification of the concept of rule-following, and curates a comprehensive benchmark, RuleBench, to evaluate a diversified range of rule-following abilities. Our experimental results on a variety of LLMs show that they are still limited in following rules. Our further analysis provides insights into the improvements for LLMs toward a better rule-following intelligent agent. The data and code can be found at: https://anonymous.4open.science/r/llm-rule-following-B3E3/

翻译：尽管大型语言模型（LLMs）已展现出强大的指令遵循能力以提供帮助，但在实际应用场景中，它们还需通过规则进行控制和引导，以确保响应的安全性与准确性。这要求LLMs具备规则遵循能力。然而，目前鲜有研究对LLMs的规则遵循能力进行清晰评估。先前试图评估LLMs规则遵循能力的研究未能明确区分规则遵循场景与指令遵循场景。因此，本文首先对规则遵循的概念进行了界定，并构建了一个综合性基准测试集RuleBench，用于评估多样化的规则遵循能力。我们在多种LLMs上的实验结果表明，它们在遵循规则方面仍存在局限。进一步的深入分析为LLMs向更优的规则遵循智能体改进提供了见解。相关数据与代码可见于：https://anonymous.4open.science/r/llm-rule-following-B3E3/