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    <abstract>Bilingual language models (LMs) offer a controlled setting for studying how training conditions shape second-language (L2) behaviour, but prior work typically varies exposure structure, scale, and architecture at once, making it difficult to attribute effects to any single factor. We introduce **Beetle**, a controlled language model pretraining framework in which tokeniser, target language, training budget, and exposure structure are each independently manipulable, enabling systematic and comparable experimentation of training conditions. Using **Beetle**, we release 285 bilingual and 45 monolingual open-source LMs with rich checkpoints across a range of exposure schedules, data scales and first languages (L1s) to study multilingual pretraining and computational modelling of bilingualism and second language learning. Evaluating models on human bilingual and second language reading-time prediction and grammaticality judgement tasks, we find that staged and temporally structured curricula consistently improve alignment with language learner reading time compared to balanced bilingual training, with the largest gains at smaller data scales and for typologically closer language pairs. The **Beetle** models are well suited tools to help move computational psycholinguistics beyond its prevailing monolingual, English-centric focus toward models of human bilingual processing, to study cross-lingual learning dynamics, while supporting community-based development of controlled model families.</abstract>
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%0 Conference Proceedings
%T Beetle: A Bilingual Model Suite for Modelling Second-Language Processing
%A Salhan, Suchir
%A Arnett, Catherine
%A Michaelov, James A.
%A Buttery, Paula
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F salhan-etal-2026-beetle
%X Bilingual language models (LMs) offer a controlled setting for studying how training conditions shape second-language (L2) behaviour, but prior work typically varies exposure structure, scale, and architecture at once, making it difficult to attribute effects to any single factor. We introduce **Beetle**, a controlled language model pretraining framework in which tokeniser, target language, training budget, and exposure structure are each independently manipulable, enabling systematic and comparable experimentation of training conditions. Using **Beetle**, we release 285 bilingual and 45 monolingual open-source LMs with rich checkpoints across a range of exposure schedules, data scales and first languages (L1s) to study multilingual pretraining and computational modelling of bilingualism and second language learning. Evaluating models on human bilingual and second language reading-time prediction and grammaticality judgement tasks, we find that staged and temporally structured curricula consistently improve alignment with language learner reading time compared to balanced bilingual training, with the largest gains at smaller data scales and for typologically closer language pairs. The **Beetle** models are well suited tools to help move computational psycholinguistics beyond its prevailing monolingual, English-centric focus toward models of human bilingual processing, to study cross-lingual learning dynamics, while supporting community-based development of controlled model families.
%U https://aclanthology.org/2026.emnlp-main.2276/
%P 51754-51782

Markdown (Informal)

[Beetle: A Bilingual Model Suite for Modelling Second-Language Processing](https://aclanthology.org/2026.emnlp-main.2276/) (Salhan et al., EMNLP 2026)

• Beetle: A Bilingual Model Suite for Modelling Second-Language Processing (Salhan et al., EMNLP 2026)

ACL

• Suchir Salhan, Catherine Arnett, James A. Michaelov, and Paula Buttery. 2026. Beetle: A Bilingual Model Suite for Modelling Second-Language Processing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 51754–51782, Budapest, Hungary. Association for Computational Linguistics.