# Do Code Models Suffer from the Dunning-Kruger Effect?

arXiv: https://arxiv.org/abs/2510.05457 · HTML: https://mlsanity.com/paper/2510.05457/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [The Dunning-Kruger Effect in Large Language Models: An Empirical Study of Confidence Calibration](https://mlsanity.com/paper/2603.09985/) (2026)

## Abstract (arXiv)

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.