@article{singh2025do,
author = {Singh, Mukul and Chatterjee, Somya and Radhakrishna, Arjun and Gulwani, Sumit},
title = {Do Code Models Suffer from the Dunning-Kruger Effect?},
year = {2025},
month = {October},
abstract = {As artificial intelligence systems increasingly collaborate with humans in creative and technical domains, questions arise about the cognitive boundaries and biases that shape our shared agency. This paper investigates the Dunning-Kruger Effect (DKE), the tendency for those with limited competence to overestimate their abilities in state-of-the-art LLMs in coding tasks. By analyzing model confidence and performance across a diverse set of programming languages, we reveal that AI models mirror human patterns of overconfidence, especially in unfamiliar or low-resource domains. Our experiments demonstrate that less competent models and those operating in rare programming languages exhibit stronger DKE-like bias, suggesting that the strength of the bias is proportionate to the competence of the models.},
url = {http://approjects.co.za/?big=en-us/research/publication/do-code-models-suffer-from-the-dunning-kruger-effect/},
journal = {ArXiv},
volume = {abs/2510.05457},
}