Conceptos Básicos
The author presents SeaEval, a benchmark for multilingual foundation models, focusing on cultural reasoning and cross-lingual consistency to assess model capabilities comprehensively.
Resumen
SeaEval introduces new evaluation criteria covering linguistic, cultural contexts, and cross-lingual consistency. Key findings highlight sensitivity to paraphrased instructions, exposure bias in label arrangements, inconsistent performance across languages, and imbalanced multilingual proficiency. The study emphasizes the need for more generalizable semantic representations and enhanced multilingual contextualization.
The content discusses the desired properties of multilingual foundation models, task selection for evaluation benchmarks, data curation process, evaluation protocols including instruction sensitivity and cross-lingual consistency metrics. Results show GPT-4 excelling in handling multilingual tasks while BLOOMZ stands out in cross-lingual consistency. Disparities in model performance across languages are observed with English usually surpassing others. Limitations include the need for more languages/cultural datasets and evaluating safety/efficiency aspects of models.
Estadísticas
SeaEval encompasses a total of 28 datasets.
Baichuan-2 model shows remarkable performance in understanding Chinese culture.
BLOOMZ demonstrates better alignment across languages but still shows unsatisfactory consistency scores.
GPT-4 excels in handling multilingual tasks with superior capabilities.
Exposure bias on label arrangements affects model predictions.
Citas
"Models exhibit varied behavior with paraphrased instructions."
"Most models give inconsistent answers when asked fact-based questions in different languages."
"GPT-4 demonstrates outstanding performance across cultures and languages."