研究修剪注意力层如何影响LLM的解释忠实性与置信度校准，揭示模型优化新视角。
arXiv:2606.24970v1 Announce Type: new Abstract: Pruning Large Language Models (LLMs) reduces memory and inference costs by removing parts of the netwo…
利用不同模型间的分歧作为无标签正确性信号，破解自信错误检测难题。
arXiv:2603.25450v2 Announce Type: replace Abstract: Detecting when a language model is wrong without ground truth labels is a fundamental challenge fo…
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…
揭露LLM置信度评估中隐含的测量偏差：概率分数与口头表达信号并非直接读取模型不确定性。
arXiv:2605.27752v1 Announce Type: new Abstract: LLM confidence calibration is often evaluated by comparing two signals: token-probability scores and v…
提出双重校准方法，同时校准LLM的知识与推理置信度，为提升模型可靠性提供全新路径。
arXiv:2601.11956v2 Announce Type: replace Abstract: Reliable reasoning in Large Language Models (LLMs) is challenged by their propensity for hallucina…