通过分析模型内部状态，让问答系统自动判断何时检索或推理。
Interpretable Uncertainty for Adaptive Retrieval and Reasoning in Question Answering
- 从模型隐藏状态中直接估算知识不足与模糊性信号
- 单次前向传播即可完成判断，效率高且避免幻觉
- 适合需要透明可解释的智能问答系统开发者
大语言模型在问答任务中表现强劲，但易产生幻觉且缺乏透明度。检索增强生成（RAG）能提升事实准确性，但何时及如何调用外部资源通常依赖不透明策略或计算开销大的多步提示。本文提出一种基于大模型内部表示显式信号的不确定性感知自适应问答框架。区分知识不足与知识模糊或冲突，并在单次前向传播中高效估计这些信号。当知识不足时触发RAG，当模糊或冲突高时进行额外推理。该方法通过可分解且高效的不确定性信号驱动决策，提供了一种透明、实用的替代方案，支持可解释用户工具的设计。
原文摘要 · Abstract (English)
Large language models (LLMs) achieve a strong performance in question answering (QA), but remain prone to hallucinations and suffer from limited transparency. Retrieval-augmented generation (RAG) can improve factuality, yet decisions about when and how to retrieve from external resources are typically based on opaque policies or computationally inefficient multi-step prompting procedures. We propose an uncertainty-aware framework for adaptive QA based on explicit signals derived from LLM internal representations. We distinguish between knowledge insufficiency and knowledge ambiguity or conflict, and efficiently estimate these from hidden states in a single forward pass. These signals guide system behaviour: RAG is triggered when knowledge is insufficient, while additional reasoning is applied when ambiguity or conflict is high. By grounding adaptive decisions in decomposed and efficiently estimable uncertainty signals, this approach provides a transparent and practical alternative to existing retrieval and reasoning strategies supporting the design of interpretable user-facing tools.
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