QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?

• URL: http://arxiv.org/abs/2503.22674v2
• Date: Fri, 24 Oct 2025 22:18:36 GMT
• Title: QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?
• Authors: Belinda Z. Li, Been Kim, Zi Wang,
• Abstract summary: Real-world queries are often underspecified and only solvable by acquiring missing information.<n>We present QuestBench, a set of underspecified reasoning tasks solvable by asking at most one question.<n>Analysis shows that the ability to solve well-specified reasoning problems is not sufficient for success on our benchmark.
• Score: 15.390695446510405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have shown impressive performance on reasoning benchmarks like math and logic. While many works have largely assumed well-defined tasks, real-world queries are often underspecified and only solvable by acquiring missing information. We formalize this information-gathering problem as a constraint satisfaction problem (CSP) with missing variable assignments. Using a special case where only one necessary variable assignment is missing, we can evaluate an LLM's ability to identify the minimal necessary question to ask. We present QuestBench, a set of underspecified reasoning tasks solvable by asking at most one question, which includes: (1) Logic-Q: logical reasoning tasks with one missing proposition, (2) Planning-Q: PDDL planning problems with partially-observed initial states, (3) GSM-Q: human-annotated grade school math problems with one unknown variable, and (4) GSME-Q: equation-based version of GSM-Q. The LLM must select the correct clarification question from multiple options. While current models excel at GSM-Q and GSME-Q, they achieve only 40-50% accuracy on Logic-Q and Planning-Q. Analysis shows that the ability to solve well-specified reasoning problems is not sufficient for success on our benchmark: models struggle to identify the right question even when they can solve the fully specified version. This highlights the need for specifically optimizing models' information acquisition capabilities.

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