大规模研究揭示LLM在多语言MCQA任务中如何从推理过程量化不确定性，跨语言表现的关键发现值得关注。
arXiv:2607.06327v1 Announce Type: cross Abstract: Uncertainty estimation (UE) enables LLM-powered systems to recognize when to abstain, yet existing r…
新方法通过证据知识蒸馏让大语言模型高效量化不确定性，无需额外推理开销
arXiv:2507.18366v2 Announce Type: replace Abstract: Accurate uncertainty quantification remains a key challenge for standard LLMs, prompting the adopt…
大模型在不确定时学会说“不知道”，选择性预测校准方法，提升安全性与可靠性。
arXiv:2607.03528v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as critical decision-making components in hig…
推翻重排序必有益的假设，提出无训练门控机制：只有不确定性高时再重排序，反而提升性能。
arXiv:2606.31087v1 Announce Type: cross Abstract: Few-shot selection typically assumes that reranking retrieved examples always improves performance. …
LLM置信度评分尺度（0-100）并非中性设计，新研究揭示其对元认知表现的显著影响。
arXiv:2603.09309v2 Announce Type: replace Abstract: Verbalized confidence, in which LLMs report a numerical certainty score, is widely used to estimat…
ICML 2026新研究用不确定性感知子空间纠正，让多模态大模型解码更可信，有效缓解流形偏离问题。
arXiv:2606.09859v1 Announce Type: cross Abstract: MLLMs frequently hallucinate objects inconsistent with visual inputs. This issue is typically attrib…
代码不仅是文本，如何评估生成代码的不确定性？这篇论文提出新方法，为代码生成任务提供更可靠的置信度估计。
arXiv:2606.09577v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as code generators, where silently wrong prog…
机器学习新方法：通过方差门控分布实现更精准的不确定性估计，为模型可靠性提供全新思路
arXiv:2509.08846v2 Announce Type: replace-cross Abstract: Evaluation of per-sample uncertainty quantification from neural networks is essential for de…
颠覆传统：让LLM先评估自身置信度再作答，大幅提升不确定性估计效率
arXiv:2603.05881v2 Announce Type: replace Abstract: Reliable deployment of large language models (LLMs) requires accurate uncertainty estimation. Exis…
当大模型没把握时，与其放弃回答，不如学会“模糊处理”——一种选择性抽象策略提升长文本生成可靠性
arXiv:2602.11908v3 Announce Type: replace Abstract: LLMs are widely used, yet they remain prone to factual errors that erode user trust and limit adop…
揭秘多标签胸部X光分类中不确定性解缠的基准研究，为医疗AI可靠性提供新视角。
arXiv:2508.04457v2 Announce Type: replace-cross Abstract: Reliable uncertainty quantification is crucial for trustworthy decision-making and the deplo…
ICLR 2026最新论文，提出超球面置信映射方法，高效提升深度学习模型的不确定性估计精度
arXiv:2605.05964v2 Announce Type: replace Abstract: Quantifying uncertainty in neural network predictions is essential for high-stakes domains such as…
利用视觉信号增强视觉-语言模型令牌级不确定性估计，提升生成稳健性，前沿研究值得关注。
arXiv:2605.27136v1 Announce Type: new Abstract: Uncertainty quantification (UQ) remains a critical challenge in Large Vision Language Models (LVLMs) f…
这篇论文系统评估了多种不确定性估计方法在检测大模型幻觉上的实际效果，为提升LLM可靠性提供关键参考。
arXiv:2605.27016v1 Announce Type: cross Abstract: Large language models (LLMs) are prone to hallucinations, i.e., statements unsupported by the input …
新方法融合分位数不确定性训练与保形校准，为成像逆问题提供统计可靠的误差界限，提升深度学习在医学影像中的可信度。
arXiv:2507.14760v2 Announce Type: replace-cross Abstract: While deep learning offers tremendous promise for scientific and medical imaging, any failur…
通过引入任务感知机制，直接在结构空间建模LLM输出，提升生成质量与不确定性估计的准确性。
arXiv:2601.21500v2 Announce Type: replace Abstract: In many applications of LLMs, natural language responses often have an underlying structure such a…
揭秘LLMs如何内部计算口头自信度：是即时响应还是自动生成？这项研究打开黑箱，深入模型推理过程。
arXiv:2603.17839v3 Announce Type: replace-cross Abstract: Verbal confidence -- prompting LLMs to state their confidence as a number or category -- is …
用语义级奖励替代二元反馈，让LLM学会表达真实不确定性，提升高安全场景下的可靠性。
arXiv:2605.15588v1 Announce Type: cross Abstract: As large language models (LLMs) are deployed in consequential settings such as medical question answ…
突破二元奖励局限，让语言模型在推理中学会表达自身不确定性，提升可解释性和可靠性。
arXiv:2507.16806v2 Announce Type: replace-cross Abstract: When language models (LMs) are trained via reinforcement learning (RL) to generate natural l…
系统探索多变量对分割不确定性估计的影响，揭示关键敏感因素，为实际应用提供可靠指导
arXiv:2605.15421v1 Announce Type: new Abstract: In this study, we explore in depth a few under-studied topics at the intersection of uncertainty estim…