Large Language Models (LLMs) have acquired ubiquitous attention for their
performances across diverse domains. Our study here searches through LLMs'
cognitive abilities and confidence dynamics. We dive deep into understanding
the alignment between their self-assessed confidence and actual performance. We
exploit these models with diverse sets of questionnaires and real-world
scenarios and extract how LLMs exhibit confidence in their responses. Our
findings reveal intriguing instances where models demonstrate high confidence
even when they answer incorrectly. This is reminiscent of the Dunning-Kruger
effect observed in human psychology. In contrast, there are cases where models
exhibit low confidence with correct answers revealing potential underestimation
biases. Our results underscore the need for a deeper understanding of their
cognitive processes. By examining the nuances of LLMs' self-assessment
mechanism, this investigation provides noteworthy revelations that serve to
advance the functionalities and broaden the potential applications of these
formidable language models.

研究通过对大型语言模型（LLMs）的认知能力和置信度动态的深入了解，揭示了模型中展现高置信度但回答错误的情况，类似于人类心理学中的邓宁 - 克鲁格效应，以及低置信度但回答正确的情况，展示了潜在的低估偏差，强调了对这些语言模型认知过程的深入研究的必要性，以进一步拓展其功能和应用领域。

大型语言模型中的自信 - 能力差距：一项认知研究

The Confidence-Competence Gap in Large Language Models: A Cognitive  Study

The dazzling promises of AI systems to augment humans in various tasks hinge
on whether humans can appropriately rely on them. Recent research has shown
that appropriate reliance is the key to achieving complementary team
performance in AI-assisted decision making. This paper addresses an
under-explored problem of whether the Dunning-Kruger Effect (DKE) among people
can hinder their appropriate reliance on AI systems. DKE is a metacognitive
bias due to which less-competent individuals overestimate their own skill and
performance. Through an empirical study (N = 249), we explored the impact of
DKE on human reliance on an AI system, and whether such effects can be
mitigated using a tutorial intervention that reveals the fallibility of AI
advice, and exploiting logic units-based explanations to improve user
understanding of AI advice. We found that participants who overestimate their
performance tend to exhibit under-reliance on AI systems, which hinders optimal
team performance. Logic units-based explanations did not help users in either
improving the calibration of their competence or facilitating appropriate
reliance. While the tutorial intervention was highly effective in helping users
calibrate their self-assessment and facilitating appropriate reliance among
participants with overestimated self-assessment, we found that it can
potentially hurt the appropriate reliance of participants with underestimated
self-assessment. Our work has broad implications on the design of methods to
tackle user cognitive biases while facilitating appropriate reliance on AI
systems. Our findings advance the current understanding of the role of
self-assessment in shaping trust and reliance in human-AI decision making. This
lays out promising future directions for relevant HCI research in this
community.

本文通过实证研究（N = 249），探讨了 DKE 是否会影响人们适当地依赖 AI 系统，以及探讨这种影响是否可以通过教程干预和利用基于逻辑单元的解释来缓解。我们发现，自我高估的参与者往往会表现出对 AI 系统的不足依赖，这阻碍了最佳团队表现。然而，教程干预对于高估自我评估的参与者来说非常有效，可以帮助他们校准自我评估并促进适当的依赖。本研究对设计解决用户认知偏差、促进在 AI 系统上适当依赖的方法具有广泛的启示性意义。