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        <title>Does Typological Distance Affect Cross-lingual Transfer?</title>
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        <namePart type="given">Pengfei</namePart>
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    <abstract>The Contrastive-Analysis Hypothesis (CAH) proposes that similarities between a learner’s first language (L1) and second language (L2) can facilitate L2 learning, while differences can make it more difficult. We ask a related computational question: under a limited, developmentally motivated data budget, does a coarse near/far relation between L1 and L2 predict grammatical performance when a language model is trained first on L1 and then on L2? We train GPT-2 Small models from scratch on English, Dutch, or Chinese with a fixed Qwen2.5 tokenizer, and continue training them on the corresponding L2 targets. Across five seeds, we compare zero-shot starting scores, terminal L2 scores, Transfer Increment (TI; terminal minus zero-shot), and Transfer Benefit (TB; terminal minus a same-budget target-only baseline). For English, NL\rightarrowEN and ZH\rightarrowEN have nearly identical TI. For Dutch, the near-L1 condition has a larger TI, but most of this difference comes from the zero-shot starting scores, while the terminal scores differ by only 0.9 percentage points. Headroom-normalized TI gives the same target-dependent picture. Across the tested pairs, the near/far contrast therefore does not produce a uniform, target-independent advantage for the near L1.</abstract>
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%0 Conference Proceedings
%T Does Typological Distance Affect Cross-lingual Transfer?
%A Ren, Pengfei
%Y Choshen, Leshem
%Y Cotterell, Ryan
%Y Gul, Mustafa Omer
%Y Jumelet, Jaap
%Y Linzen, Tal
%Y Mueller, Aaron
%Y Salhan, Suchir
%Y Shah, Raj Sanjay
%Y Warstadt, Alex
%Y Wilcox, Ethan Gotlieb
%S Proceedings of the Second BabyLM Workshop
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-475-0
%F ren-2026-typological
%X The Contrastive-Analysis Hypothesis (CAH) proposes that similarities between a learner’s first language (L1) and second language (L2) can facilitate L2 learning, while differences can make it more difficult. We ask a related computational question: under a limited, developmentally motivated data budget, does a coarse near/far relation between L1 and L2 predict grammatical performance when a language model is trained first on L1 and then on L2? We train GPT-2 Small models from scratch on English, Dutch, or Chinese with a fixed Qwen2.5 tokenizer, and continue training them on the corresponding L2 targets. Across five seeds, we compare zero-shot starting scores, terminal L2 scores, Transfer Increment (TI; terminal minus zero-shot), and Transfer Benefit (TB; terminal minus a same-budget target-only baseline). For English, NL\rightarrowEN and ZH\rightarrowEN have nearly identical TI. For Dutch, the near-L1 condition has a larger TI, but most of this difference comes from the zero-shot starting scores, while the terminal scores differ by only 0.9 percentage points. Headroom-normalized TI gives the same target-dependent picture. Across the tested pairs, the near/far contrast therefore does not produce a uniform, target-independent advantage for the near L1.
%U https://aclanthology.org/2026.babylm-main.6/
%P 65-74

Markdown (Informal)

[Does Typological Distance Affect Cross-lingual Transfer?](https://aclanthology.org/2026.babylm-main.6/) (Ren, BabyLM 2026)

• Does Typological Distance Affect Cross-lingual Transfer? (Ren, BabyLM 2026)

ACL

• Pengfei Ren. 2026. Does Typological Distance Affect Cross-lingual Transfer?. In Proceedings of the Second BabyLM Workshop, pages 65–74, Budapest, Hungary. Association for Computational Linguistics.