AI BriefingAi2 releases XYBench, a benchmark for testing LLMs' ability to identify and correct user misconceptions
·2026.11.14 02:02
Key point
Developed by Ai2 and partners, XYBench contains 8,115 questions to measure how well models recognize when a user's request is based on a misconception and steer them toward the underlying goal.
Details
- XYBench is a new benchmark of 8,115 questions designed to test if AI assistants can identify the "XY problem," where a user asks for help with a mistaken approach (Y) instead of their actual goal (X).
- Evaluation of Claude Sonnet 4.6, GPT-5, Gemini 3.1 Pro, Qwen3.5-9B, and DeepSeek-V4 Pro showed that while models often provided solutions to the stated request, they rarely redirected users to the underlying goal.
- On Stack Overflow questions, models focused primarily on the underlying goal in only 35–41% of responses, compared to 47–57% where they focused on the stated request.
- An oracle experiment where the intended goal was explicitly provided improved solution inclusion but did not consistently close the gap with human reference answers on misconception identification.
- The benchmark measures three aspects: pragmatic solution (addressing both Y and X), pragmatic emphasis (focus on Y vs X), and misconception identification (steering away from Y).
- XYBench won the Best Resource Paper Award at EMNLP 2026 and is available on Hugging Face and GitHub.
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