Perceptions of Linguistic Uncertainty by Language Models and Humans (2407.15814v2)

Abstract: Uncertainty expressions such as "probably" or "highly unlikely" are pervasive in human language. While prior work has established that there is population-level agreement in terms of how humans quantitatively interpret these expressions, there has been little inquiry into the abilities of LLMs in the same context. In this paper, we investigate how LLMs map linguistic expressions of uncertainty to numerical responses. Our approach assesses whether LLMs can employ theory of mind in this setting: understanding the uncertainty of another agent about a particular statement, independently of the model's own certainty about that statement. We find that 7 out of 10 models are able to map uncertainty expressions to probabilistic responses in a human-like manner. However, we observe systematically different behavior depending on whether a statement is actually true or false. This sensitivity indicates that LLMs are substantially more susceptible to bias based on their prior knowledge (as compared to humans). These findings raise important questions and have broad implications for human-AI and AI-AI communication.