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 across multiple QA datasets show that the finetuned planner achieves significant improvements in macro F1 and abstention recall while reducing hallucination rates, under a compute-constrained training regime. These results provide strong empirical evidence that epistemic decision-making must be learned during training rather than imposed at inference time.
Abstract (translated)
大语言模型（LLMs）在问答和检索增强生成（RAG）任务中已展现出卓越性能，但其隐含假设是用户查询已完全明确且可回答。在实际场景中，查询往往不完整、模糊或缺失关键变量，导致模型产生过度自信或幻觉式回复。本研究聚焦于信息不完整条件下的决策感知型查询解析，要求模型决定是回答、请求澄清还是回避。我们发现，标准及增强型RAG系统均未可靠展现此类认知意识，即便信息不足仍倾向于生成回答。为此，我们提出PassiveQA三动作框架，通过监督微调使模型行为与信息充分性对齐。该方法整合了结构化信息状态表示、知识图谱支撑的上下文，以及显式建模缺失变量与决策推理的微调规划器。在多项问答数据集上的实验表明，在算力受限的训练条件下，微调后的规划器在宏F1和回避召回率上取得显著提升，同时降低了幻觉率。这些结果为认知决策必须在训练阶段习得而非推理时强加提供了有力实证依据。
URL
https://arxiv.org/abs/2604.04565
