The common approach to communicate a large language model's (LLM) uncertainty is to add a percentage number or a hedging word to its response. But is this all we can do? Instead of generating a single answer and then hedging it, an LLM that is fully transparent to the user needs to be able to reflect on its internal belief distribution and output a summary of all options it deems possible, and how likely they are. To test whether LLMs possess this capability, we develop the SelfReflect metric, an information-theoretic distance between a given summary and a distribution over answers. In interventional and human studies, we find that SelfReflect indicates even slight deviations, yielding a fine measure of faithfulness between a summary string and an LLM's actual internal distribution over answers. With SelfReflect, we make a resounding negative observation: modern LLMs are, across the board, incapable of revealing what they are uncertain about, neither through reasoning, nor chains-of-thoughts, nor explicit finetuning. However, we do find that LLMs are able to generate faithful summaries of their uncertainties if we help them by sampling multiple outputs and feeding them back into the context. This simple approach shines a light at the universal way of communicating LLM uncertainties whose future development the SelfReflect score enables. To support the development of this universal form of LLM uncertainties, we publish the code that implements our metric for arbitrary LLMs under https://github.com/apple/ml-selfreflect .

翻译：传达大型语言模型（LLM）不确定性的常见方法是在其响应中添加百分比数字或模糊限制词。但这难道就是我们所能做的全部吗？一个对用户完全透明的LLM不应仅生成单一答案后加以修饰，而需能够反思其内部信念分布，并输出所有它认为可能的选项及其相应概率的摘要。为测试LLM是否具备这种能力，我们开发了SelfReflect指标——一种衡量给定摘要与答案分布之间信息理论距离的度量方法。通过干预性研究和人工评估，我们发现SelfReflect能检测出细微偏差，从而精确衡量摘要字符串与LLM实际内部答案分布之间的忠实度。借助SelfReflect，我们得出一个明确的否定结论：现代LLM普遍无法揭示其不确定的内容，无论是通过推理、思维链还是显式微调均告失败。然而，我们发现若通过采样多个输出并将其反馈至上下文来辅助LLM，它们能够生成忠实反映其不确定性的摘要。这种简单方法为传达LLM不确定性的通用方式指明了方向，而SelfReflect评分为该方向的未来发展提供了支持。为促进这种通用形式的不确定性传达机制的发展，我们在https://github.com/apple/ml-selfreflect 发布了适用于任意LLM的指标实现代码。