LLM自我提问框架提升风险控制效果
When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control
这篇论文介绍了一种叫CoSQ的新方法，能让大语言模型在不确定时主动不回答问题，这个方法在多个模型上测试过，效果不错。
这篇论文提出了一种名为Chain-of-Self-Questioning（CoSQ）的框架，通过让大语言模型在回答问题前先自我提问来评估信息需求。在TruthfulQA测试集上，Grounded-CoSQ方法将模型不正确承诺的比例从13.1%降低到8.9%，同时提高了回答准确率。
When Should LLMs Abstain? Chain-of-Self-Questioning for Selective Risk Control
Large language models can produce fluent answers when their factual support is weak. This paper introduces Chain-of-Self-Questioning (CoSQ), a prompt-only framework that makes answer commitment conditional on an explicit assessment of the information required to answer a question. We evaluate three CoSQ variants under seventeen conditions on the 817-item TruthfulQA multiple-choice validation set using eleven open-weight and hosted model families. In the final balanced-option protocol, Grounded-CoSQ at τ=0.90 reduces the mean unconditional wrong-commitment rate from 13.1% under chain-of-thought prompting to 8.9%, a 32.1% relative reduction, while increasing answered accuracy from 86.9% to 89.7% and answering 87.6% of questions. Both improvements hold for all eleven models and at every evaluated threshold. Critical-CoSQ and Adaptive-CoSQ provide neighboring operating points with 88.6% and 86.5% coverage, respectively, while remaining more reliable than the baseline. A secondary Natural Questions Short-Answer evaluation provides convergent open-form evidence. These findings show that self-assessment can support explicit, tunable answer-or-abstain decisions when an unsupported commitment is more costly than referral or review.