SOTAVerified

A Comprehensive Evaluation of Cognitive Biases in LLMs

2024-10-20Code Available1· sign in to hype

Simon Malberg, Roman Poletukhin, Carolin M. Schuster, Georg Groh

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

We present a large-scale evaluation of 30 cognitive biases in 20 state-of-the-art large language models (LLMs) under various decision-making scenarios. Our contributions include a novel general-purpose test framework for reliable and large-scale generation of tests for LLMs, a benchmark dataset with 30,000 tests for detecting cognitive biases in LLMs, and a comprehensive assessment of the biases found in the 20 evaluated LLMs. Our work confirms and broadens previous findings suggesting the presence of cognitive biases in LLMs by reporting evidence of all 30 tested biases in at least some of the 20 LLMs. We publish our framework code to encourage future research on biases in LLMs: https://github.com/simonmalberg/cognitive-biases-in-llms

Tasks

Reproductions