How Overconfidence in Initial Choices and Underconfidence Under Criticism Modulate Change of Mind in Large Language Models

Dharshan Kumaran, Stephen M. Fleming, Larisa Markeeva, Joseph Heyward, Mrinal Mathur, Andrea Banino, Razvan Pascanu, Simon Osindero, Benedetto De Martino, Petar Veličković, Viorica Patraucean

Abstract:

Large language models (LLMs) exhibit strikingly conflicting behaviors: they can appear steadfastly
overconfident in their initial answers whilst at the same time being prone to excessive doubt when
challenged. To investigate this apparent paradox, we developed a novel experimental paradigm, exploiting the unique ability to obtain confidence estimates from LLMs without creating memory of their
initial judgments – something impossible in human participants. We show that LLMs – Gemma 3,
GPT4o and o1-preview – exhibit a pronounced choice-supportive bias that reinforces and boosts their
estimate of confidence in their answer, resulting in a marked resistance to change their mind. We
further demonstrate that LLMs markedly overweight inconsistent compared to consistent advice, in
a fashion that deviates qualitatively from normative Bayesian updating. Finally, we demonstrate that
these two mechanisms – a drive to maintain consistency with prior commitments and hypersensitivity
to contradictory feedback – parsimoniously capture LLM behavior in a different domain. Together, these
findings furnish a mechanistic account of LLM confidence that explains both their stubbornness and
excessive sensitivity to criticism.