PQR：一个生成多样化且逼真用户查询的框架，以引发问答代理失败
PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures
AI总结：
PQR框架通过迭代交互生成多样化且逼真的用户查询，以发现问答代理的失败案例，其方法比现有方法更有效。
摘要：
评估基于LLM的代理仍然具有挑战性，因为识别有意义的失败案例通常需要大量的人力来设计现实的测试场景。先前的工作主要关注自动发现由对抗性用户引起的代理失败，而忽略了具有真实用户意图的查询也触发代理失败的情况。我们引入PQR，一个框架，不仅能够针对特定目标（如有用性、安全性等）揭示代理失败，还能模拟真实用户意图。PQR通过两个互补模块的迭代交互运作。查询精炼模块执行重写以探索多样化的查询变体，而提示精炼模块利用先前反馈推导新的违反目标的策略和现实性政策以精炼提示，从而生成引发失败但逼真的查询。我们在检测电子商务问答代理的不帮助性响应上评估了PQR。我们的方法发现了23% - 78%更多的不帮助性响应，且我们生成的查询比先前方法更加多样化和逼真。
英文摘要：
Evaluating LLM-based agents remains challenging because identifying meaningful failure cases often requires substantial human effort to design realistic test scenarios. Prior works primarily focus on automatically discovering agent failures induced by adversarial users, while overlooking queries with real user intents that also trigger agent failures. We introduce PQR, a framework that not only surfaces agent failures with respect to specific objectives (e.g., helpfulness, safety, etc.) but also resembles real users' intents. PQR operates through an iterative interaction between two complementary modules. The query refinement module performs rewrites to explore diverse query variations, while the prompt refinement module uses prior feedback to derive new objective-violating strategies and realism policies for refining prompts, which in turn generate failure-triggering yet realistic queries. We evaluate PQR on detecting an e-commerce QA agent's unhelpful responses. Our method uncovers 23% - 78% more unhelpful responses, and our generated queries are more diverse and realistic compared to previous methods.
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。
