All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages (2411.16508v2)

Abstract: Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource languages, all while effectively integrating corresponding visual cues. In pursuit of culturally diverse global multimodal models, our proposed All Languages Matter Benchmark (ALM-bench) represents the largest and most comprehensive effort to date for evaluating LMMs across 100 languages. ALM-bench challenges existing models by testing their ability to understand and reason about culturally diverse images paired with text in various languages, including many low-resource languages traditionally underrepresented in LMM research. The benchmark offers a robust and nuanced evaluation framework featuring various question formats, including true/false, multiple choice, and open-ended questions, which are further divided into short and long-answer categories. ALM-bench design ensures a comprehensive assessment of a model's ability to handle varied levels of difficulty in visual and linguistic reasoning. To capture the rich tapestry of global cultures, ALM-bench carefully curates content from 13 distinct cultural aspects, ranging from traditions and rituals to famous personalities and celebrations. Through this, ALM-bench not only provides a rigorous testing ground for state-of-the-art open and closed-source LMMs but also highlights the importance of cultural and linguistic inclusivity, encouraging the development of models that can serve diverse global populations effectively. Our benchmark is publicly available.

Evaluating Large Multimodal Models on a Diverse Array of 100 Languages

The paper "All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages" addresses the burgeoning yet nuanced field of Large Multimodal Models (LMMs) by introducing the All Languages Matter Benchmark (ALM-bench). As LMMs progress, it becomes imperative to ensure these models comprehend diverse cultural contexts and include low-resource languages, alongside effectively integrating corresponding visual information. This text offers a systematic evaluation platform for LMMs across a broad spectrum of 100 languages, aiming to foster cultural inclusivity and linguistic diversity.

Introduction and Motivation

In the field of LMMs, there is currently a significant focus on widely spoken languages with abundant training data, which often leads to a lack of robust performance in low-resource languages and nuanced cultural contexts. Addressing this concern, ALM-bench provides an extensive benchmarking framework that scrutinizes the cultural understanding and linguistic reasoning abilities of LMMs. This is achieved through a curated dataset featuring visual questions and answers (VQAs) across different domains and formats, ensuring the measurement of models' performance in both high-resource and low-resource settings.

Benchmark Design and Dataset

ALM-bench is an ambitious project, encompassing 100 languages across 73 countries, utilizing 24 scripts and 15 language families. The benchmark distinguishes itself by focusing on both generic and culturally specific categories, capturing an impressive array of 19 domains ranging from daily life and architecture to literature and music. This stress on cultural depth makes ALM-bench a vital tool to assess LMMs' capability in culturally rich and diverse scenarios. Additionally, achieving this breadth required the collaboration of over 60 volunteers, ensuring cultural relevance and accuracy integrated into the dataset through extensive human annotation, translating more than 22,763 QA pairs manually reviewed by native language speakers.

Methodology and Evaluation

The paper benchmarks 16 state-of-the-art LMMs, with evaluations focusing on four question types: multiple choice questions (MCQs), true/false questions, short VQAs, and long VQAs. The results reveal considerable disparities between high and low-resource languages. Notably, closed-source models like GPT-4o excel over open-source counterparts, achieving higher overall accuracy. A notable finding lies in models' substantial performance drop when tasked with culturally specific content, highlighting an area ripe for further research and improvement. This gap underlines the existing challenges these models face in comprehending culturally nuanced language inputs, especially from less represented scripts and language families.

Implications and Future Directions

The paper emphasizes the importance of developing and fine-tuning LMMs that are culturally aware and linguistically diverse. This is crucial for deploying AI models responsibly on a global scale, as diverse language understanding allows these technologies to serve a wider array of users more effectively. The authors discuss the role of visual context in improving LMMs' performance, pointing towards future developments in integrating geocultural metadata to further enhance model accuracy.

The ALM-bench introduces not only an enriched dataset but also a benchmark that marks an advancement in culturally and linguistically inclusive AI research. It calls to attention the gap in the current LMMs' capabilities and presents an opportunity for researchers to focus efforts on bridging these divides. As the field progresses, efforts should not only aim to widen the linguistic coverage but also encapsulate cultural intelligence within AI, thus aligning technological progress with societal inclusivity. This benchmarking framework represents a key step towards this objective, laying the groundwork for more culturally adaptable and linguistically comprehensive AI systems.