Musical Ethnocentrism in Large Language Models (2024)

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.

## introduction it has long been known that machine learning models pick up and thus perpetuate human biases in various ways, most prominently by learning them from their training data. for text-based models, even early embedding approaches exhibited e.g. gender bias @xcite . with the recent rise of large language models (llms), gender and race biases were quickly discovered and analyzed in various domains @xcite @xcite . types of bias that have been considered somewhat less include those based on culture and geography. however, @xcite recently showed that llms also exhibit those, both implicitly and explicitly. their research demonstrated that when prompted to rate random locations on earth on various characteristics, llms generally yielded lower ratings for certain regions, e.g. the global south. there appear to be correlations with the coverage of regions and their cultural and historical significance in the training data, statistics across a range of aspects around the world, and possibly structural biases of the institutions creating these models, which are mainly based in north america and europe. we hypothesize that such biases are not only present for purely geographic topics, but also for cultural developments in different regions of the world. in this paper, we conduct first experiments to detect such biases with regards to music culture. those are based on two different types of measurements, translated into prompts: a) asking models to give an overview of top musical artists, and b) asking models to rate aspects of musical culture in different regions of the world. the first experiment elicits model bias on an open-ended question with regards to presence of different cultural regions in the models, while the second one employs a direct comparison to extract implicit judgments learned by the models. to gain insights into the influence of where and how the model was trained, we prompt two models from different regions of the world in four languages. the rest of the paper is structured as follows: section 2 gives an overview of other work in the field of geocultural biases in llms. section 3 provides details of our experimental design. the results are presented in section 4. finally, sections 5 and 6 discuss our findings and make suggestions for future work. ## related work initial studies, such as those discussed in @xcite and @xcite , show llms often encode biases favoring western, english-speaking norms, impacting their fairness and representation of non-western cultures as well as performance on non-western topics, like traditional chinese medicine @xcite . further research seeks methodologies to measure and mitigate these biases more accurately. the interdisciplinary approach in @xcite and the survey on modeling culture in llms @xcite propose new frameworks for understanding and adjusting the embedded cultural values in llms. the "culturepark" @xcite initiative and the "normad" @xcite benchmark are notable in their attempts to simulate cross-cultural communication scenarios and assess llms' adaptability to cultural contexts through synthetic story generation, providing novel approaches to evaluating cultural sensitivity and adaptability in ai technologies. the "cdeval" @xcite benchmark specifically addresses the need to evaluate the cultural dimensions of llms, integrating automated generation and human verification to assess cultural traits across multiple domains. similarly, the "culturellm" @xcite project aims to fine-tune llms on culturally diverse data. in the music domain, @xcite illustrates domain-specific biases, arguing for more comprehensive benchmarks in varied knowledge domains. ## methodology in this section, we will describe our experimental design, including used models, prompt design, prompted tasks, and postprocessing of the results. ## discussion as expected, we observed a strong dominance of the western world, particularly the u.s., in both tasks. south america was comparatively wellrepresented, whereas asia and africa were almost never mentioned in the "top 100" experiments, and rated consistently lower in the second experiment. both models produce slightly different results, with mixtral appearing a bit more diverse. however, there is some indication that state-of-the-art language models are trained on most of the text data currently available on the internet, meaning that the cultural distribution of training data may not vary too much between any current models foot_0 . the language in which prompts are given to the model does appear to play a role, but not in a very straightforward way (e.g. chinese-language prompting does not lead to china being mentioned significantly more often). due to the cross-lingual abilities of llms, the language of the context may in fact play a smaller role than language in the training data. commoncrawl, often named as the biggest source of llm training data, contains around 46% english-language text, which the second-most frequent language being russian at just 6% 2 . nevertheless, the language of a country will in all probability be implicitly somewhat more strongly associated with its culture. singers gpt, en gpt, cn mixtral, fr complexity gpt, en gpt, cn mixtral, fr these results may not seem like a big issue at first glance, but could lead to undesired effects in downstream tasks, e.g. when used in recommendation pipelines or for assistance in writing about culture. this is particularly insidious because a) these biases are then much harder to detect, and b) they will lead to an amplification of biases already present in existing, human-produced material. on the other hand, users may in fact expect models to behave the way they currently do, especially when considering the "top 100" experiment. the question then becomes whether future models should maintain those biases, or could potentially serve to offer a more diverse and educative view of the world to their users. this will become a more pressing consideration with future individualization of models. ## future work in this work, we presented a first step towards detecting cultural biases in llms with a focus on the music domain. as mentioned above, it would be very interesting to see whether llms from other parts of the world (first and foremost china) perpetuate the same biases. future work could also analyze other music-related tasks around the world, and compare with other aspects of culture. on a smaller note, the results were obtained via the online interfaces of the models which may filter or change results; future work could employ the models directly for more control. beyond analyzing these biases, an important research goal lies in mitigating them. when considering the "top 100" task, this is very subjective. an interesting research direction may be aimed more towards human-computer interaction: what do users expect when prompting models for recommendations like these? from an ethical standpoint, should models then fulfil users' expectations, or aim for more diversity than what a human author or the training data may provide? answers may lie in integrating external knowledge sources (e.g. knowledge graphs) into llms, but also in adapting them towards individual users. for the ratings task, possible solutions are much harder to determine. in principle, the whole task of rating musical cultures is not well-posed, but it reveals underlying judgments learned by the model, which may influence downstream tasks (including the "top 100" experiment). removing these judgments may be impossible as they appear to be implicit in the training data. a possible future direction may lie in making these influences more transparent to users, allowing them to decide for themselves whether the model's answer is based on the correct assumptions @xcite .