Beyond Instruction Following: Evaluating Rule Following of Large Language Models
Abstract: Although Large Language Models (LLMs) have demonstrated strong \textbf{instruction-following} ability to be helpful, they are further supposed to be controlled and guided by $\textbf{rules}$ in real-world scenarios to be safe, and accurate in responses. This demands the possession of $\textbf{rule-following}$ 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, $\textbf{RuleBench}$, 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/
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: large language model, instruction following, rule following, evaluation, benchmark
Contribution Types: Model analysis & interpretability, NLP engineering experiment, Reproduction study, Data resources
Languages Studied: English, Chinese
Submission Number: 2236
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