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    <abstract>Recent work has proposed explicitly inducing language-wise modularity in multilingual LMs via sparse fine-tuning (SFT) on per-language subnetworks as a means of better guiding cross-lingual sharing. In this paper, we investigate (1) the degree to which language-wise modularity *naturally* arises within models with no special modularity interventions, and (2) how cross-lingual sharing and interference differ between such models and those with explicit SFT-guided subnetwork modularity. In order to do so, we use XLM-R as our multilingual LM. Moreover, to quantify language specialization and cross-lingual interaction, we use a Training Data Attribution method that estimates the degree to which a model’s predictions are influenced by in-language or cross-language training examples. Our results show that language-specialized subnetworks do naturally arise, and that SFT, rather than always increasing modularity, can decrease language specialization of subnetworks in favor of more cross-lingual sharing.</abstract>
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%0 Conference Proceedings
%T Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks
%A Choenni, Rochelle
%A Shutova, Ekaterina
%A Garrette, Dan
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F choenni-etal-2024-examining
%X Recent work has proposed explicitly inducing language-wise modularity in multilingual LMs via sparse fine-tuning (SFT) on per-language subnetworks as a means of better guiding cross-lingual sharing. In this paper, we investigate (1) the degree to which language-wise modularity *naturally* arises within models with no special modularity interventions, and (2) how cross-lingual sharing and interference differ between such models and those with explicit SFT-guided subnetwork modularity. In order to do so, we use XLM-R as our multilingual LM. Moreover, to quantify language specialization and cross-lingual interaction, we use a Training Data Attribution method that estimates the degree to which a model’s predictions are influenced by in-language or cross-language training examples. Our results show that language-specialized subnetworks do naturally arise, and that SFT, rather than always increasing modularity, can decrease language specialization of subnetworks in favor of more cross-lingual sharing.
%U https://aclanthology.org/2024.findings-naacl.21
%P 287-301

Markdown (Informal)

[Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks](https://aclanthology.org/2024.findings-naacl.21) (Choenni et al., Findings 2024)

• Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks (Choenni et al., Findings 2024)

ACL

• Rochelle Choenni, Ekaterina Shutova, and Dan Garrette. 2024. Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 287–301, Mexico City, Mexico. Association for Computational Linguistics.