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Is Cross-Lingual Transfer in Bilingual Models Human-Like? A Study with Overlapping Word Forms in Dutch and English

arXiv 2604.07067 ↗First posted 8 Apr 2026cs.CLcited by 0

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Abstract (arXiv)

Bilingual speakers show cross-lingual activation during reading, especially for words with shared surface form. Cognates (friends) typically lead to facilitation, whereas interlingual homographs (false friends) cause interference or no effect. We examine whether cross-lingual activation in bilingual language models mirrors these patterns. We train Dutch-English causal Transformers under four vocabulary-sharing conditions that manipulate whether (false) friends receive shared or language-specific embeddings. Using psycholinguistic stimuli from bilingual reading studies, we evaluate the models through surprisal and embedding similarity analyses. The models largely maintain language separation, and cross-lingual effects arise primarily when embeddings are shared. In these cases, both friends and false friends show facilitation relative to controls. Regression analyses reveal that these effects are mainly driven by frequency rather than consistency in form-meaning mapping. Only when just friends share embeddings are the qualitative patterns of bilinguals reproduced. Overall, bilingual language models capture some cross-linguistic activation effects. However, their alignment with human processing seems to critically depend on how lexical overlap is encoded, possibly limiting their explanatory adequacy as models of bilingual reading.

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References · 13

  1. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  2. 2019How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddingscited by 770
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  5. 2019Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERTcited by 301
  6. 2019Cross-Lingual Ability of Multilingual BERT: An Empirical Studycited by 182
  7. 2021All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Qualitycited by 150
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  9. 2020XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualizationcited by 42
  10. 2025Large Language Models Are Human-Like Internallycited by 28
  11. 2025Causal Estimation of Tokenisation Biascited by 16
  12. 2022Entropy- and Distance-Based Predictors From GPT-2 Attention Patterns Predict Reading Times Over and Above GPT-2 Surprisalcited by 14
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  1. 2023Second Language Acquisition of Neural Language Modelscited by 8

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