GuideBench: Benchmarking Domain-Oriented Guideline Following for LLM Agents

• URL: http://arxiv.org/abs/2505.11368v2
• Date: Tue, 17 Jun 2025 06:55:16 GMT
• Title: GuideBench: Benchmarking Domain-Oriented Guideline Following for LLM Agents
• Authors: Lingxiao Diao, Xinyue Xu, Wanxuan Sun, Cheng Yang, Zhuosheng Zhang,
• Abstract summary: Large language models (LLMs) have been widely deployed as autonomous agents capable of following user instructions and making decisions in real-world applications.<n>GuideBench is a benchmark designed to evaluate guideline following performance of LLMs.
• Score: 22.390137173904943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have been widely deployed as autonomous agents capable of following user instructions and making decisions in real-world applications. Previous studies have made notable progress in benchmarking the instruction following capabilities of LLMs in general domains, with a primary focus on their inherent commonsense knowledge. Recently, LLMs have been increasingly deployed as domain-oriented agents, which rely on domain-oriented guidelines that may conflict with their commonsense knowledge. These guidelines exhibit two key characteristics: they consist of a wide range of domain-oriented rules and are subject to frequent updates. Despite these challenges, the absence of comprehensive benchmarks for evaluating the domain-oriented guideline following capabilities of LLMs presents a significant obstacle to their effective assessment and further development. In this paper, we introduce GuideBench, a comprehensive benchmark designed to evaluate guideline following performance of LLMs. GuideBench evaluates LLMs on three critical aspects: (i) adherence to diverse rules, (ii) robustness to rule updates, and (iii) alignment with human preferences. Experimental results on a range of LLMs indicate substantial opportunities for improving their ability to follow domain-oriented guidelines.

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