Large language models often respond to ambiguous requests by implicitly committing to one interpretation. Intent misunderstandings can frustrate users and create safety risks. To address this, we propose generating multiple interpretation-answer pairs in a single structured response to ambiguous requests. Our models are trained with reinforcement learning and customized reward functions using multiple valid answers as supervision. Experiments on conversational question answering and semantic parsing demonstrate that our method achieves higher coverage of valid answers than baseline approaches. Human evaluation confirms that predicted interpretations are highly aligned with their answers. Our approach promotes transparency with explicit interpretations, achieves efficiency by requiring only one generation step, and supports downstream applications through its structured output format.

翻译：大型语言模型在应对模糊请求时，常会隐含地采纳单一解释进行回应。意图误解可能导致用户挫败感并引发安全风险。为解决此问题，我们提出针对模糊请求，在单个结构化响应中生成多组“解释-答案”对。我们采用强化学习框架，以多个有效答案为监督信号设计定制化奖励函数来训练模型。在对话式问答与语义解析任务上的实验表明，本方法相比基线方案能覆盖更多有效答案。人工评估证实，模型预测的解释与对应答案高度一致。本方法通过显式解释提升透明度，仅需单步生成实现高效响应，并借助结构化输出格式为下游应用提供支持。