# Cognitive bias in large language models: Cautious optimism meets anti-Panglossian meliorism

arXiv: https://arxiv.org/abs/2311.10932 · HTML: https://mlsanity.com/paper/2311.10932/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 61 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://mlsanity.com/paper/2203.13474/) (2022)
- [Capturing Failures of Large Language Models via Human Cognitive Biases](https://mlsanity.com/paper/2202.12299/) (2022)
- [Challenging the appearance of machine intelligence: Cognitive bias in LLMs and Best Practices for Adoption](https://mlsanity.com/paper/2304.01358/) (2023)

## Built on by (most cited first, 4 of 4)

- [Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges](https://mlsanity.com/paper/2409.02387/) (2024)
- [Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?](https://mlsanity.com/paper/2410.23856/) (2024)
- [Large Language Model Benchmarks in Medical Tasks](https://mlsanity.com/paper/2410.21348/) (2024)
- [Explaining Large Language Models Decisions Using Shapley Values](https://mlsanity.com/paper/2404.01332/) (2024)

## Abstract (arXiv)

Traditional discussions of bias in large language models focus on a conception of bias closely tied to unfairness, especially as affecting marginalized groups. Recent work raises the novel possibility of assessing the outputs of large language models for a range of cognitive biases familiar from research in judgment and decisionmaking. My aim in this paper is to draw two lessons from recent discussions of cognitive bias in large language models: cautious optimism about the prevalence of bias in current models coupled with an anti-Panglossian willingness to concede the existence of some genuine biases and work to reduce them. I draw out philosophical implications of this discussion for the rationality of human cognitive biases as well as the role of unrepresentative data in driving model biases.