{"ID":2860154,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.05457","arxiv_id":"2510.05457","title":"Do Code Models Suffer from the Dunning-Kruger Effect?","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.05457","url_pdf":"https://arxiv.org/pdf/2510.05457v1","authors":"[\"Mukul Singh\",\"Somya Chatterjee\",\"Arjun Radhakrishna\",\"Sumit Gulwani\"]","published":"2025-10-06T23:41:24Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CL\",\"cs.SE\"]","methods":"[\"Large Language Model\"]","has_code":false}