English(EN) XYBench: Can LLMs Respond Pragmatically to Queries with Misconceptions?

新基准揭示大型语言模型难以处理用户误解

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员推出了XYBench，这是一个旨在评估大型语言模型（LLMs）处理包含误解的查询的能力的新基准，这是一种常见的XY问题。该基准包含来自技术和日常领域的8,115个查询，评估LLMs识别误解和提供务实、预期解决方案的能力。实验表明，即使是领先的LLMs也经常直接回应字面请求而非根本问题，并且与人类相比，在识别用户误解方面存在显著困难。 AI

影响凸显了当前LLMs的一个关键局限性，表明需要改进务实推理和用户意图理解。

排序理由该条目描述了一篇介绍用于评估LLM能力的新基准的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Akhila Yerukola, Jena D. Hwang, Mingqian Zheng, Jenna Godsey, Hyunwoo Kim, Valentina Pyatkin, Jennifer Hu, Maarten Sap · 2026-09-10 04:00

XYBench：大型语言模型能否务实地回应带有误解的查询？

arXiv:2609.06842v1 Announce Type: cross Abstract: When non-expert users ask LLMs for assistance, their queries can often have misconceptions (e.g., "How do I parse XML with regex?"). In such cases, often referred to as the XY-problem, LLMs must identify the misconception ("regex …

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报道来源 [1]

XYBench：大型语言模型能否务实地回应带有误解的查询？

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