询问、放松还是行动？评估LLM偏好推理中的可操作不确定性
Ask, Relax, or Act? Evaluating Actionable Indeterminacy in LLM Preference Reasoning
- The Chinese University of Hong Kong Shenzhen（香港中文大学（深圳））
- Beijing University of Posts and Telecommunications（北京邮电大学）
- Halmstad University College（哈尔姆斯塔德大学学院）
- Tongji University（同济大学）
- University of Waterloo（滑铁卢大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究形式化LLM偏好推理中的可操作不确定性，构建基于求解器的基准测试，发现模型常在不必要时干预，且明确响应要求能显著提升完全正确响应，强调可靠智能体需仅在必要时行动。
AI中文摘要：
一个LLM智能体能够识别不确定性，却仍可能选择错误的下一步：在行动已有充分依据时选择询问，或在约束必须改变时寻求澄清。我们将可操作不确定性形式化：当所有可接受的偏好或目标共享某个已接受行动时，采取行动；当每种可能性都可行但无共享行动时，进行澄清；当请求不可行时，提出最小成本的允许约束修复方案。我们构建了一个基于求解器的基准测试，涵盖对象分配、会议调度、公寓选择和稳定匹配。匹配对在保留相同来源的同时，改变是否需要干预，评估则区分决策正确性、匹配对可靠性和完全正确响应。我们的发现揭示了一个反复出现的困难：识别何时无需干预——模型能够识别需要澄清或修复的情况，却仍在已存在合理行动时进行干预。正确的决策标签也无法保证可用的行动、问题或修复。关键在于，响应要求不仅影响决策的表达方式，还影响所做的决策。明确所需内容可显著提高完全正确响应，并可能改变干预决策，即使输出已可解析。这些发现强调，可靠的智能体能力需要的不仅是识别不确定性：它需要在必要时才干预，并将选定的下一步转化为可验证的响应。
英文摘要：
An LLM agent can recognize uncertainty yet still choose the wrong next step: asking when action is already justified, or seeking clarification when the constraints must change. We formalize actionable indeterminacy: act when an accepted action is shared across all admissible preferences or objectives, clarify when each possibility is feasible but no action is shared, and propose a minimum-cost permitted constraint repair when the request is infeasible. We construct a solver-grounded benchmark spanning object allocation, meeting scheduling, apartment choice, and stable matching. Matched pairs retain the same source while changing whether intervention is necessary, and evaluation separates decision correctness, matched-pair reliability, and fully correct responses. Our findings reveal a recurring difficulty in recognizing when intervention is unnecessary: models can identify situations requiring clarification or repair yet still intervene when a justified action already exists. Correct decision labels also fail to guarantee usable actions, questions, or repairs. Crucially, response requirements shape not only how decisions are expressed but also which decisions are made. Making the required content explicit substantially improves fully correct responses and can change intervention decisions, even when outputs are already parseable. These findings highlight that reliable agency requires more than recognizing uncertainty: it requires intervening only when necessary and translating the chosen next step into a verifiable response.
