XYBench: Can LLMs Respond Pragmatically to Queries with Misconceptions?
Akhila Yerukola, Jena D. Hwang, Mingqian Zheng, Jenna Godsey, Hyunwoo Kim, Valentina Pyatkin, Jennifer Hu, Maarten Sap
cs.CL · cs.AI
Quick verdict
8,115-query benchmark shows LLMs answer literal requests yet fail pragmatic redirection; well validated, but LLM-built ground truth tempers the gap claims.
Summary
The paper introduces XYBENCH, an 8,115-query benchmark for the 'XY problem': queries whose stated request (S-request) embodies a misconception about the user's actual goal (I-request). Data comprise Stack Overflow (1,557), Stack Exchange (184), synthetic WikiHow scenarios (6,272), and 102 manually curated queries. Grounded in cooperative response theory, each query carries three reference dimensions — S-solution, misconception identification, and I-solution — extracted from human answers by a two-stage Claude Sonnet 4.5 pipeline, filtered by a GPT-5 oracle multiple-choice step, and human-validated (filtering κ=0.764; SO extraction accuracy 91–95%; 68.5% of WikiHow items fully correct). Across five frontier models, responses reliably address stated requests (0.57–0.91) but far less often intended ones (0.33–0.71); misconception identification lags human references badly (≤63% vs 79–90% on SO/SE; ≤52% vs 79% on the manual set), and pragmatic solutions are rarely emphasized. Models prefer pragmatic answers in forced choice (up to 84%) yet fail to generate them; oracle intent provision helps without closing the gap. Limitations include LLM-built ground truth judged by the same model family, no human references for WikiHow (77% of data), English-only scope, and an abstract-versus-Table 2 inconsistency (0.75–0.92 vs 0.57–0.91).
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Assessment at a glance
5
risk flags identified
● high 0
● medium 3
● low 2
5 strengths · 5 weaknesses · 5 author questions
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Key claims under review
- Even the strongest LLMs predominantly answer the literal stated request (S-solution presence 0.57–0.91 in Table 2) and far less often address the user's intended goal (0.33–0.71).
- Models identify misconceptions far less often than human reference answers: at most 63% vs 79–90% on Stack Overflow/Exchange and at most 52% vs 79% on the manually curated subset.
- Models overwhelmingly prefer pragmatic responses in a multiple-choice setting (up to 84%) yet consistently fail to generate such responses themselves, revealing a knowing-doing gap.
- Oracle provision of explicit S-/I-requests improves pragmatic generation but does not close the human gap, indicating that both intent detection and intent-conditional generation are bottlenecks.
Strengths
Novel, well-motivated capability target
Procedural misconceptions (the 'XY problem') are distinct from factoid false-presupposition work (§2.1) and grounded in cooperative response theory (Joshi et al., 1986); the S-request/I-request/three-dimension design (§3.1) cleanly operationalizes the construct.
Multi-stage human validation at every pipeline step
MCQ filtering κ=0.764 with 93% GPT-5 agreement (§3.2, Table 3); SO extraction 91–95% strict accuracy, 84% fully correct across 5 fields (Table 4); WikiHow IAA α=0.98–1.00 with 68.5% fully correct (Table 5); judge validation on 500 responses (Table 7).
Judge design anticipates known LLM-judge failure modes
Experiments that separate detection from action
Honest internal reporting
Weaknesses
Human-baseline circularity
Abstract/table numeric inconsistency
No human references for 77% of the benchmark
Thin inferential statistics on headline gaps
Memorization and curation risks acknowledged but thinly mitigated
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Scope & Claims
XYBENCH (arXiv 2609.06842) is a benchmark of 8,115 English queries whose stated request (S-request) embodies a procedural misconception about the asker's real goal (I-request) — the 'XY problem' familiar from Stack Overflow culture. Grounded in cooperative response theory (Joshi et al., 1986), each item carries three reference dimensions: an S-solution, an I-solution, and an S-I misconception identification (§3.1). The paper claims that (1) frontier LLMs overwhelmingly answer the literal request (S-solution presence 0.57–0.91 across five models and four sources, Table 2) but address the intended goal far less often (0.33–0.71); (2) they identify misconceptions far less often than human references (≤63% vs 79–90% on SO/SE; ≤52% vs 79% on the 102-item manual set); (3) they prefer pragmatic responses when forced to choose (up to 84%, Figs 17–20) yet fail to generate them; and (4) an oracle supplying explicit intent improves but does not close the gap (Fig 3, Table 11). Notably, the abstract and §1 quote an S-solution range of 0.75–0.92 that matches neither Table 2 nor §4 (0.57–0.91) — a small but real internal inconsistency in a headline number.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
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Frequently asked questions
What is the XY problem that XYBENCH tests?
The XY problem is when a user asks how to execute a self-chosen method (X) that reflects a misconception, while their real goal is Y — e.g., asking how to parse XML with regex when the actual goal is extracting data, for which an XML parser is the right tool. XYBENCH measures whether LLMs detect such misconceptions and redirect users toward the intended goal instead of just answering literally.
How large is XYBENCH and where does its data come from?
8,115 English queries: 1,557 from Stack Overflow, 184 from 17 Stack Exchange subdomains, 6,272 synthetically generated from WikiHow how-to articles, and 102 manually curated from forums and personal experience. Each item is annotated with a stated request, an intended request, and three response dimensions (stated solution, misconception identification, intended solution).
How well do LLMs perform on XYBench?
Across five frontier models, 0.57–0.91 of responses address the literal request but only 0.33–0.71 address the intended goal. Misconception identification reaches at most 63% versus 79–90% for human reference answers on Stack Overflow/Exchange. Models pick the pragmatic response in multiple choice up to 84% of the time but fail to generate such responses, and even when given the user's explicit intent they still trail humans.
Is XYBENCH publicly available?
The paper states the benchmark will be released under CC BY 4.0, and all construction and evaluation prompts are reproduced in the appendices. The provided text names no code repository, and re-running the evaluation requires paid access to proprietary LLM APIs (Claude, GPT-5, Gemini), so independent replication depends on those resources.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Main results and ablation tables were flattened by PDF extraction into running rows; values were matched to columns by header order, so spot-checks against the published PDF are advisable.
- Appendix H annotation-interface screenshots (Figures 21–25) contain no extractable content in the provided text; this does not affect any cited numbers.
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