PQR Framework Generates Realistic User Queries to Test QA Agent Failures
Researchers introduced PQR, a framework that automatically generates diverse, realistic user queries designed to expose failures in large language model-based question-answering agents. The framework uses iterative refinement between query and prompt modules to create failure-triggering queries that resemble genuine user intents rather than adversarial attacks. This approach is significant because it addresses a gap in LLM agent evaluation by identifying meaningful failure cases without requiring extensive manual effort.
PQR is a new evaluation framework that addresses a key challenge in testing LLM-based agents: identifying realistic failure cases without substantial human effort. Rather than focusing solely on adversarial attacks, the framework generates queries that reflect real user intents while still triggering agent failures across multiple objectives such as helpfulness and safety. The system operates through two complementary modules—a query refinement module that explores diverse query variations, and a prompt refinement module that derives new failure-inducing strategies based on prior feedback. When evaluated on an e-commerce QA agent, PQR uncovered 23% to 78% more unhelpful responses compared to previous methods, and the generated queries demonstrated greater diversity and realism. This work addresses an important gap in LLM evaluation methodology by automating the discovery of meaningful failure modes.
What's missing
The study's limitations regarding generalization to other types of QA agents, domains beyond e-commerce, or different failure categories are not discussed in the abstract. Additionally, the specific metrics used to measure 'realism' and 'diversity' of generated queries are not detailed.
What different sources said
- arXiv cs.CLCenter
PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures
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