Knowing What's Missing: Assessing Information Sufficiency in Question Answering

ArXi:2512.06476v3 Announce Type: replace 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 reliable, implicit signal for assessing overall sufficiency.