PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures
PQR is a framework that generates diverse and realistic user queries to uncover failures in QA agents, using iterative interaction between query refinement and prompt refinement modules. It detects 23-78% more unhelpful responses in e-commerce QA agents, with greater diversity and realism than prior methods.
[2605.16551] PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures
[Submitted on 15 May 2026]
Title:PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures
View a PDF of the paper titled PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures, by Yunan Lu and 4 other authors
View PDF
Abstract: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.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2605.16551 [cs.CL]
(or arXiv:2605.16551v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.16551
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yunan Lu [view email] [v1] Fri, 15 May 2026 18:50:43 UTC (1,288 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled PQR: A Framework to Generate Diverse and Realistic User Queries that Elicit QA Agent Failures, by Yunan Lu and 4 other authors
View PDF
TeX Source
view license
Current browse context:
cs.CL
new | recent | 2026-05
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)