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XYBench: Helping with the problem behind the question

AchievementBenchmarkNov 13, 2026

XYBench, developed with researchers at Carnegie Mellon, NVIDIA, and Johns Hopkins, won the Best Resource Paper Award at EMNLP 2026. The benchmark contains 8,115 real-world questions drawn from Stack Overflow, Stack Exchange, manually curated examples, and WikiHow guides, testing whether AI assistants recognize requests based on implicit misconceptions—the XY problem—and redirect users toward their actual goals. Across programming and everyday tasks, evaluated models more often answered the stated request than the underlying goal.

Why it matters

XYBench, a benchmark developed with researchers at Carnegie Mellon, NVIDIA, and Johns Hopkins, won the Best Resource Paper Award at EMNLP 2026. It tests whether AI assistants recognize the XY problem: situations where a user asks about an assumed approach when their real goal lies elsewhere, such as asking how to thicken frosting when the cupcakes are simply too warm to frost. The benchmark draws on real-world questions from Stack Overflow, Stack Exchange, manually curated examples, and WikiHow guides, spanning programming and everyday tasks like cooking and home repair. It measures whether models identify misconceptions, prioritize solutions to the underlying goal, and steer users away from mistaken approaches. The evaluated models frequently answered the stated request while neglecting the user's actual objective, performing worse than human reference answers. The work highlights a pragmatic gap: assistants must infer intent from context, not just execute literal instructions, to genuinely help users who do not know the right question to ask.

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Evidence

1source· awaiting independent confirmation

No score is assigned. Sources and their independence are shown in the citation chain below.

Citation chain · 1 source

01med
huggingface.coBloghttps://huggingface.co/blog/allenai/xybenchThis source is the benchmark's own release post, explaining the XY problem concept, the construction of the dataset from forum and WikiHow questions, and the pragmatic evaluation metrics used to judge model responses.
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NVIDIACompanyCarnegie MellonCompanyJohns HopkinsCompany

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Canonical: https://huggingface.co/blog/allenai/xybench