Offscript: LLMs指令遵循自动审计
Offscript: Automated Auditing of Instruction Adherence in LLMs
AI总结：
Offscript通过自动化审计检测LLM指令遵循问题，揭示86.4%的对话中存在潜在违规行为，其中22.2%经人工确认为实质性违规。
摘要：
大型语言模型（LLMs）和生成式搜索系统越来越多地被用于信息检索，由具有不同知识来源和呈现偏好的多样化群体使用。尽管用户可以通过自定义指令和行为提示来定制LLM的行为，但目前没有机制可以评估这些指令是否被有效遵循。我们提出了Offscript，一种自动化审计工具，能够高效地识别LLM中潜在的指令遵循失败。在一项分析来自Reddit的自定义指令的试点研究中，Offscript在86.4%的对话中检测到潜在的偏离指令行为，其中22.2%通过人工审查被确认为实质性违规。我们的发现表明，自动化审计是评估与信息检索相关的行为指令合规性的一种可行方法。
英文摘要：
Large Language Models (LLMs) and generative search systems are increasingly used for information seeking by diverse populations with varying preferences for knowledge sourcing and presentation. While users can customize LLM behavior through custom instructions and behavioral prompts, no mechanism exists to evaluate whether these instructions are being followed effectively. We present Offscript, an automated auditing tool that efficiently identifies potential instruction following failures in LLMs. In a pilot study analyzing custom instructions sourced from Reddit, Offscript detected potential deviations from instructed behavior in 86.4% of conversations, 22.2% of which were confirmed as material violations through human review. Our findings suggest that automated auditing serves as a viable approach for evaluating compliance to behavioral instructions related to information seeking.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
