SaudiCulture: A Benchmark for Evaluating Large Language Models Cultural Competence within Saudi Arabia (2503.17485v1)

Abstract: LLMs have demonstrated remarkable capabilities in natural language processing; however, they often struggle to accurately capture and reflect cultural nuances. This research addresses this challenge by focusing on Saudi Arabia, a country characterized by diverse dialects and rich cultural traditions. We introduce SaudiCulture, a novel benchmark designed to evaluate the cultural competence of LLMs within the distinct geographical and cultural contexts of Saudi Arabia. SaudiCulture is a comprehensive dataset of questions covering five major geographical regions, such as West, East, South, North, and Center, along with general questions applicable across all regions. The dataset encompasses a broad spectrum of cultural domains, including food, clothing, entertainment, celebrations, and crafts. To ensure a rigorous evaluation, SaudiCulture includes questions of varying complexity, such as open-ended, single-choice, and multiple-choice formats, with some requiring multiple correct answers. Additionally, the dataset distinguishes between common cultural knowledge and specialized regional aspects. We conduct extensive evaluations on five LLMs, such as GPT-4, Llama 3.3, FANAR, Jais, and AceGPT, analyzing their performance across different question types and cultural contexts. Our findings reveal that all models experience significant performance declines when faced with highly specialized or region-specific questions, particularly those requiring multiple correct responses. Additionally, certain cultural categories are more easily identifiable than others, further highlighting inconsistencies in LLMs cultural understanding. These results emphasize the importance of incorporating region-specific knowledge into LLMs training to enhance their cultural competence.

• The paper introduces SaudiCulture, a benchmark to evaluate large language models' cultural competence within Saudi Arabia, revealing they struggle with region-specific and culturally nuanced questions.
• The study evaluated five LLMs, including GPT-4, using SaudiCulture, finding decreased performance on cultural questions and regional variations, with GPT-4 scoring 66% in the west and 36% in the north.
• The findings highlight the need for culturally aware AI and integrating diverse data, positioning SaudiCulture as a resource to improve LLM adaptability and encouraging adaptation to other cultural settings.

Evaluating Cultural Competence in LLMs with SaudiCulture

The paper "SaudiCulture: A Benchmark for Evaluating LLMs' Cultural Competence within Saudi Arabia" presents a substantial contribution to the field of NLP by introducing SaudiCulture, a benchmark designed to assess the cultural competence of LLMs within the context of Saudi Arabia. This work seeks to explore the extent to which LLMs can comprehend and represent the cultural diversity inherent in Saudi society.

Objectives and Contribution

The authors identify a critical gap in the current capabilities of LLMs: their limited proficiency in handling culturally nuanced content, especially for non-Western cultures. To address this, SaudiCulture is developed as a comprehensive dataset comprising questions that reflect the cultural heterogeneity of five major Saudi regions—North, South, Central, East, and West—covering various cultural domains such as food, clothing, entertainment, and celebrations. The dataset includes open-ended, single-choice, and multiple-choice questions, facilitating an in-depth evaluation of LLMs' cultural knowledge and inferential abilities.

Methodology and Findings

The paper evaluates five LLMs: GPT-4, Llama 3.3, FANAR, Jais, and AceGPT. These models were assessed based on their performance across different regions and cultural categories. A key finding is that all models show a significant decrease in performance when tasked with answering culturally specialized or region-specific questions, particularly those requiring multiple correct answers. GPT-4, a widely recognized state-of-the-art model, demonstrated the best overall performance with an accuracy of 66% in the western region and struggled more with the northern region, where its accuracy dropped to 36%.

Interestingly, this disparity in performance across regions highlights the uneven representation of cultural nuances within the training data of these models, underscoring the necessity of integrating culturally diverse data to enhance model performance. Moreover, the results revealed an inherent challenge in capturing contextually rich and specialized cultural knowledge, a testament to the complexity of encoding cultural nuances in LLMs.

Implications

The findings of this paper emphasize the pressing need for culturally aware AI systems, particularly in the domain of NLP. By illustrating the current limitations of LLMs in understanding and representing the cultural intricacies of Saudi Arabia, this paper sets the stage for future research aimed at creating more inclusive and representative LLMs. The development of SaudiCulture offers a foundational resource that other researchers can leverage to improve cultural adaptability in LLMs.

Future Directions

As this benchmark is grounded in the Saudi context, future research could expand this work by adapting the methodology to other diverse cultural settings, further expanding the scope and applicability of culturally informed AI tools. It also poses potential for exploring more sophisticated fine-tuning techniques and embedding strategies that enhance models' retention of cultural context, paving the way for more nuanced and globally representative AI systems.

In conclusion, the SaudiCulture benchmark represents a significant stride towards enhancing LLMs' cultural competence. It encourages a reevaluation of training methodologies to incorporate cultural subtleties effectively, fostering the development of models that are not only technically proficient but also culturally contextual and sensitive.