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We present SeaEval, a benchmark for multilingual foundation models. Inaddition to characterizing how these models understand and reason with naturallanguage, we also investigate how well they comprehend cultural practices,nuances, and values. Alongside standard accuracy metrics, we investigate thebrittleness of foundation models in the dimensions of semantics andmultilinguality. Our analyses span both open-sourced and closed models, leadingto empirical results across classic NLP tasks, reasoning, and culturalcomprehension. Key findings indicate (1) Most models exhibit varied behaviorwhen given paraphrased instructions. (2) Many models still suffer from exposurebias (e.g., positional bias, majority label bias). (3) For questions rooted infactual, scientific, and commonsense knowledge, consistent responses areexpected across multilingual queries that are semantically equivalent. Yet,most models surprisingly demonstrate inconsistent performance on these queries.(4) Multilingually-trained models have not attained "balanced multilingual"capabilities. Our endeavors underscore the need for more generalizable semanticrepresentations and enhanced multilingual contextualization. SeaEval can serveas a launchpad for more thorough investigations and evaluations formultilingual and multicultural scenarios.

SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning