Evaluating Cultural Commonsense Reasoning Across Diverse Indonesian Provinces

Core Concepts

Even the best open-source language models struggle to comprehend the diverse cultures across eleven Indonesian provinces, with the highest accuracy reaching only 53.2%. Incorporating location context significantly enhances model performance, especially in larger models like GPT-4.

Abstract

This paper introduces IndoCulture, a novel dataset to evaluate cultural commonsense reasoning across eleven Indonesian provinces. The dataset was manually developed by local experts in each province based on predefined topics. The key highlights and insights from the study are: All open-source language models, including multilingual and Indonesian-centric models, exhibit limited understanding of Indonesian cultures, in contrast to the 100% accuracy achieved by human experts. The multiple-choice question method generally outperforms the sentence completion method, with exceptions for some smaller Indonesian-centric models. Incorporating location context, especially at the province level, significantly boosts the performance of larger language models like GPT-4, emphasizing the importance of geographical context in commonsense reasoning. Models perform better on cultures from specific provinces like Bali and West Java, likely due to the abundance of training data on these regions, highlighting the risk of cultural biases in language models. Analysis on fine-grained cultural elements shows that models struggle the most with understanding cultural norms and language-specific aspects, compared to other elements like artifacts and rituals. Manual evaluation of model-generated explanations reveals a significant gap between the models' ability to select the correct answer and provide a reasonable justification, especially for open-source models. The findings underscore the challenging nature of the IndoCulture dataset and the need for more inclusive and geographically-aware language models that can effectively reason about diverse cultural contexts.

Stats

The fat bodies of female dancers are believed symbols of prosperity. The fat body of female dancers is believed symbols of beauty. Emi secluded herself in the forest due to the Korowai tribe's belief that pregnant women were vulnerable to attacks by evil spirits. Aldia wore a rencong around her waist.

Quotes

"Culture is a multifaceted concept encompassing the way of life, including our thoughts and actions." "Indonesia is a highly multicultural country, home to over 1,300 recognized ethnic groups and more than 700 languages."

Key Insights Distilled From

IndoCulture

by Fajri Koto,R... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01854.pdf

Deeper Inquiries

How can language models be trained to better capture the nuances and complexities of diverse cultural contexts beyond the dominant English-centric perspective?

Language models can be trained to better capture diverse cultural contexts by incorporating data from a wide range of languages and cultures during the training process. This can involve creating datasets that represent various cultural nuances, traditions, beliefs, and practices from different regions around the world. By including diverse cultural data, language models can learn to understand and generate text that is more culturally sensitive and accurate. Additionally, fine-tuning models on specific cultural datasets can help them adapt to the intricacies of different cultures, enabling them to provide more contextually appropriate responses.

What are the potential biases and limitations in the current approaches to constructing cultural commonsense reasoning datasets, and how can they be addressed?

One potential bias in current approaches to constructing cultural commonsense reasoning datasets is the overrepresentation of certain cultures, particularly English-centric ones, leading to a lack of diversity and inclusivity. This can result in models performing poorly on tasks related to underrepresented cultures. To address this, dataset creators should prioritize collecting data from a wide range of cultures and languages to ensure a more balanced representation. Additionally, implementing rigorous quality control measures, involving experts from diverse cultural backgrounds in dataset creation, and conducting thorough evaluations can help mitigate biases and limitations in cultural datasets.

Given the significance of geographical and cultural contexts in commonsense reasoning, how can language models be designed to seamlessly integrate and leverage such contextual information to enhance their overall reasoning capabilities?

Language models can be designed to seamlessly integrate geographical and cultural contexts by incorporating location-specific information into the training and inference processes. This can involve providing models with location cues or prompts to help them understand the cultural background and context of the text they are processing. By leveraging geographical and cultural information, models can make more informed decisions and generate responses that are culturally appropriate and contextually relevant. Additionally, incorporating multi-modal data, such as images or videos, that capture cultural nuances can further enhance the models' understanding of diverse contexts.