PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures

2026-05-19 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

PQR is a novel framework designed to generate diverse and realistic user queries that specifically elicit failures in LLM-based Question-Answering (QA) agents. Unlike previous methods that focus on adversarial user inputs, PQR aims to identify agent failures stemming from real user intents. The framework operates through an iterative process involving two modules: a query refinement module that explores diverse query variations through rewrites, and a prompt refinement module that uses feedback to develop new objective-violating strategies and realism policies. These policies then guide the generation of failure-triggering yet realistic queries. Evaluated on an e-commerce QA agent, PQR demonstrated significant improvements, uncovering 23% to 78% more unhelpful responses and generating queries that were both more diverse and realistic than those produced by prior methods.

Key takeaway

For AI Engineers evaluating LLM-based QA agents, PQR offers a robust method to uncover a broader spectrum of failures. You should consider integrating PQR's iterative query and prompt refinement approach to generate more realistic and diverse test scenarios, moving beyond purely adversarial testing. This will help you identify and mitigate agent shortcomings related to actual user intents, improving overall agent reliability and user satisfaction.

Key insights

PQR generates realistic, diverse user queries to uncover LLM-based QA agent failures beyond adversarial inputs.

Principles

• Iterative refinement improves query generation.
• Real user intent queries reveal distinct failures.

Method

PQR uses iterative interaction between a query refinement module (rewrites for diversity) and a prompt refinement module (feedback-driven strategies for realism and failure elicitation) to generate test queries.

In practice

• Test QA agents for unhelpful responses.
• Generate diverse, realistic test cases.
• Identify objective-violating agent behaviors.

Topics

• PQR Framework
• LLM Agent Evaluation
• Query Generation
• Failure Detection
• Prompt Refinement

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.