Determining whether a provided context contains sufficient information to answer a question is a critical challenge for building reliable question-answering systems. While simple prompting strategies have shown success on factual questions, they frequently fail on inferential ones that require reasoning beyond direct text extraction. We hypothesize that asking a model to first reason about what specific information is missing provides a more reliable, implicit signal for assessing overall sufficiency. To this end, we propose a structured Identify-then-Verify framework for robust sufficiency modeling. Our method first generates multiple hypotheses about missing information and establishes a semantic consensus. It then performs a critical verification step, forcing the model to re-examine the source text to confirm whether this information is truly absent. We evaluate our method against established baselines across diverse multi-hop and factual QA datasets. The results demonstrate that by guiding the model to justify its claims about missing information, our framework produces more accurate sufficiency judgments while clearly articulating any information gaps.

翻译：判断给定上下文是否包含足够信息以回答问题，是构建可靠问答系统的关键挑战。尽管简单提示策略在事实性问题上已展现成效，但对于需要超越直接文本提取的推理型问题，这些策略往往失效。我们假设，首先要求模型推理具体缺失哪些信息，能为评估整体充分性提供更可靠的隐式信号。为此，我们提出一种结构化的“识别-验证”框架，用于鲁棒的充分性建模。该方法首先生成关于缺失信息的多个假设并建立语义共识，随后执行关键验证步骤，强制模型重新检查源文本以确认该信息是否确实缺失。我们在多种多跳推理与事实性问答数据集上，将本方法与现有基线进行对比评估。结果表明，通过引导模型论证其关于缺失信息的判断，我们的框架能产生更准确的充分性评估，同时清晰阐明任何信息缺口。