Multilingual capabilities of GPT: A study of structural ambiguity.

This study examines the multilingual capabilities of GPT, focusing on its handling of syntactic ambiguity across English, Korean, and Japanese. We investigate whether GPT can capture language-specific attachment preferences or if it relies primarily on English-centric training patterns. Using ambiguous relative clauses as a testing ground, we assess GPT's interpretation tendencies across language contexts. Our findings reveal that, while GPT (GPT-3.5-turbo, GPT-4-turbo, GPT 4o)'s performance aligns with native English speakers' preferred interpretations, it overgeneralizes this interpretation in Korean and lacks clear preferences in Japanese, despite distinct attachment biases among native speakers of these languages. The newer, smaller-scale models-o1-mini and o3-mini-further reinforce this trend by closely mirroring English attachment patterns in both Korean and Japanese. Overall results suggest that GPT's multilingual proficiency is limited, likely reflecting a bias toward high-resource languages like English, although differences in model size and tuning strategies may partially mitigate the extent of English-centric generalization. While GPT models demonstrate aspects of human-like language processing, our findings underscore the need for further refinement to achieve a more nuanced engagement with linguistic diversity across languages.