What are the challenges of evaluating multilingual Vision-Language Models?

Evaluating multilingual vision-language models (VLMs) presents unique challenges due to the interplay of visual and textual data across diverse languages. These challenges stem from data limitations, linguistic complexity, and the difficulty of measuring cross-cultural understanding. Addressing these issues requires careful consideration of how models process and align multimodal inputs in varied linguistic contexts.

A primary challenge is the uneven availability and quality of multilingual training and evaluation data. While English-centric datasets like COCO or Visual Genome are well-curated, equivalent resources for other languages are scarce or incomplete. For example, image-text pairs in low-resource languages (e.g., Swahili or Bengali) often lack sufficient volume and diversity, leading to biased evaluations. Even when translated, nuances like idiomatic expressions or culturally specific references may not carry over accurately. Annotators must also account for regional variations—a term like “football” might refer to soccer in British English but American football in U.S. contexts. These gaps make it hard to ensure models truly understand concepts across languages rather than relying on superficial translations.

Another issue involves designing evaluation metrics that fairly assess performance across languages. Traditional metrics like BLEU or CIDEr, designed for monolingual settings, struggle with multilingual comparisons due to structural differences between languages. For instance, a correct German description might use different word order than its English counterpart, penalizing the model unfairly. Human evaluations, while more reliable, become impractical at scale due to the need for multilingual experts. Additionally, tasks like cross-lingual retrieval (e.g., finding images that match a Thai caption using a Spanish query) require testing bidirectional alignment capabilities, which existing benchmarks often don’t systematically cover. Without standardized multilingual benchmarks, comparing models becomes inconsistent.

Finally, multilingual VLMs risk inheriting biases from their training data and failing to generalize across cultures. Models trained primarily on Western-centric images and text may misinterpret symbols or practices from other regions. For example, a model might incorrectly label a traditional Indian garment as a “costume” due to training data bias. Linguistic structural differences, such as right-to-left scripts (Arabic) or agglutinative morphology (Turkish), can also cause processing errors. Testing these models requires culturally diverse test cases and stress-testing edge cases in underrepresented languages. Without addressing these gaps, evaluations may overstate a model’s multilingual capability, masking critical weaknesses in real-world usage.