[2604.04565] PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervised Finetuning

Computer Science > Computation and Language arXiv:2604.04565 (cs) [Submitted on 6 Apr 2026] Title:PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervised Finetuning Authors:Madhav S Baidya View a PDF of the paper titled PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervised Finetuning, by Madhav S Baidya View PDF HTML (experimental) Abstract:Large Language Models (LLMs) have achieved strong performance in question answering and retrieval-augmented generation (RAG), yet they implicitly assume that user queries are fully specified and answerable. In real-world settings, queries are often incomplete, ambiguous, or missing critical variables, leading models to produce overconfident or hallucinated responses. In this work, we study decision-aware query resolution under incomplete information, where a model must determine whether to Answer, Ask for clarification, or Abstain. We show that standard and enhanced RAG systems do not reliably exhibit such epistemic awareness, defaulting to answer generation even when information is insufficient. To address this, we propose PassiveQA, a three-action framework that aligns model behaviour with information sufficiency through supervised finetuning. Our approach integrates structured information-state representations, knowledge graph-grounded context, and a finetuned planner that explicitly models missing variables and decision reasoning. Experiments acr...