A Comprehensive Evaluation of Cognitive Biases in LLMs

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Malberg, Simon, Poletukhin, Roman, Schuster, Carolin M., Groh, Georg
Format: Preprint
Published: 2024
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866917053009494016
author Malberg, Simon
Poletukhin, Roman
Schuster, Carolin M.
Groh, Georg
author_facet Malberg, Simon
Poletukhin, Roman
Schuster, Carolin M.
Groh, Georg
contents 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
format Preprint
id arxiv_https___arxiv_org_abs_2410_15413
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Comprehensive Evaluation of Cognitive Biases in LLMs
Malberg, Simon
Poletukhin, Roman
Schuster, Carolin M.
Groh, Georg
Computation and Language
Artificial Intelligence
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
title A Comprehensive Evaluation of Cognitive Biases in LLMs
topic Computation and Language
Artificial Intelligence
url https://arxiv.org/abs/2410.15413