Musical ethnocentrism in Large Language Models

TL;DR

This paper investigates geocultural biases in Large Language Models, focusing on musical preferences, revealing a strong Western bias through experiments with ChatGPT and Mixtral.

Contribution

It introduces the first analysis of musical geocultural biases in LLMs, highlighting their tendency to favor Western music cultures.

Findings

LLMs prefer Western music cultures in top contributor lists.

LLMs rate Western musical cultures more favorably.

Bias towards Western music is consistent across different experiments.

Abstract

Large Language Models (LLMs) reflect the biases in their training data and, by extension, those of the people who created this training data. Detecting, analyzing, and mitigating such biases is becoming a focus of research. One type of bias that has been understudied so far are geocultural biases. Those can be caused by an imbalance in the representation of different geographic regions and cultures in the training data, but also by value judgments contained therein. In this paper, we make a first step towards analyzing musical biases in LLMs, particularly ChatGPT and Mixtral. We conduct two experiments. In the first, we prompt LLMs to provide lists of the "Top 100" musical contributors of various categories and analyze their countries of origin. In the second experiment, we ask the LLMs to numerically rate various aspects of the musical cultures of different countries. Our results indicate a strong preference of the LLMs for Western music cultures in both experiments.