Bilingual Language Models Capture Cross-Linguistic Influence in Second-Language Reading
While monolingual language model (LM) surprisal is widely established as a predictor of reading times, it cannot capture the non-native (L2) reading dynamics that are shaped by the reader's first language (L1). In this work, we investigate whether the cross-lingual transfer within bilingual models aligns with human L1-driven biases during L2 reading. We evaluate bilingual model
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While monolingual language model (LM) surprisal is widely established as a predictor of reading times, it cannot capture the non-native (L2) reading dynamics that are shaped by the reader's first language (L1). In this work, we investigate whether the cross-lingual transfer within bilingual models aligns with human L1-driven biases during L2 reading. We evaluate bilingual model
This research highlights the importance of L1-specific fine-tuning for L2 reading tasks, suggesting that generic multilingual models might not be optimal for nuanced cross-lingual applications. For ML engineers, understanding how to leverage and evaluate cross-lingual transfer in bilingual models can lead to more accurate and user-specific NLP solutions, especially in education or global content platforms.