Self-consistency decoding enhances LLMs' performance on reasoning tasks by sampling diverse reasoning paths and selecting the most frequent answer. However, it is computationally expensive, as sampling many of these (lengthy) paths is required to increase the chances that the correct answer emerges ...
本文介绍了一个名为SaySelf的训练框架,旨在教导大型语言模型(LLMs)以更准确和细致的方式表达其信心,并能够自省地提供推理过程中的不确定性。与之前的工作相比,SaySelf不仅提高了LLMs的信心估计准确性,还能促使它们在遇到不确定情况时,能够产生明确的自我反思性理由来解释其答案和信心水平。该框架通过两个主要阶段实现...
将confidence 用于 LLMs 的 Self-Refine 可以有效提升其性能。 2 Confidence Estimation Methods Likelihood-based Confidence:奖输出tokens的联合概率分布作为confidence。 True Probability Confidence:让LLMs自行判断其输出是否为“True”。 Self-verbalized Confidence:包括 Verbal Number(如:0.95)和Verbal Word(如:“low...
Odds ratios and 95% confidence intervals for the associations between the dimensions of the downsizing process and depressive symptoms.M. Harvey BrennerElena AndreevaTöres TheorellMarcel GoldbergHugo WesterlundConstanze LeineweberLinda L. Magnusson. ...
直接Prompt LLMs 输出置信度水平。 2.4Surrogate Token Probability 可以看作 Sequence Probabilities 和 Verbalization的混合,输入Prompt要求模型提供特定token作为输出,以报告输入中声明的真实性;然后使用分配给这些token的概率来确定置信度。 2.5 Output Consistency ...