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.
Figures & tables
Figure 1: Vocabulary conditions. Full overlap (A) : All identical surface forms that appear in Dutch and English are shared between the two languages, each represented by a single embedding. Friends Overlap (B) : Only cognates and loan words are shared. False Friends Overlap (C) : Only false friends are shared. Minimal Overlap (D) : Only punctuation and named entities are shared, while other tokens have language-specific embeddings. Across all conditions, punctuation and named entities are shared (denoted as ∗ ).
Figure 2: The frequency of each word in the Dutch and English portion of the LM training data. The frequencies are generally positively correlated: a word form that is frequent in Dutch is frequent in English, but more so for friends than false friends . The plot includes (false) friend words from psycholinguistic stimuli ( Bultena et al., 2014 ; Huisman, 2025 ) as well as other compiled lists and our training data (here labeled as Corpus).
Figure 3: Cosine similarity between Dutch and English across the context and word-only embeddings for training data samples for target words (friends or false friends). A larger similarity means the LM considers Dutch and English embeddings more similar.
Figure 4: Surprisal estimates for friends/controls and false friends/controls from LMs trained under different vocabulary conditions. The symbol ∗ indicates a statistically significant difference ( p<.05 ) between the two word types.
Condition
Vocabulary size
Num. parameters
A
77,369
144,672,000
B
141,877
194,214,144
C
144,170
195,975,168
D
144,681
196,367,616
Table 1: Vocabulary size and number of trainable parameters for the language model trained in each condition.
Figure 5: Training and evaluation loss.
Figure 6: Mean pooling over the embeddings in the preceding tokens withing each sentence ( n=500 ). Mean pooling over these to obtain a context embeddings from English samples μCEN for the token winter . The same was performed over Dutch sentences to obtain μCNL(winter) .
Figure 7: Mean pooling over the token representations of the target word in n English sentences ( n=500 ) to obtain μWEN(winter) . Similarly, n Dutch sentences are used to calculate μWNL(winter) .