As natural language becomes the default interface for human-AI interaction,
there is a critical need for LMs to appropriately communicate uncertainties in
downstream applications. In this work, we investigate how LMs incorporate
confidence about their responses via natural language and how downstream users
behave in response to LM-articulated uncertainties. We examine publicly
deployed models and find that LMs are unable to express uncertainties when
answering questions even when they produce incorrect responses. LMs can be
explicitly prompted to express confidences, but tend to be overconfident,
resulting in high error rates (on average 47%) among confident responses. We
test the risks of LM overconfidence by running human experiments and show that
users rely heavily on LM generations, whether or not they are marked by
certainty. Lastly, we investigate the preference-annotated datasets used in
RLHF alignment and find that humans have a bias against texts with uncertainty.
Our work highlights a new set of safety harms facing human-LM interactions and
proposes design recommendations and mitigating strategies moving forward.

AI 与人类交互时，自然语言作为默示接口，需要全面地传达语言模型关于回复的不确定性。研究发现，现有的语言模型在回答问题时无法表达不确定性，导致高错误率。人类在实验中过分依赖于语言模型生成的回复，而且存在对不确定性回复的偏见。因此，该研究提出设计建议和缓解策略，以应对人类与语言模型互动中的新一套安全危害。