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.- Anthology ID:
- 2024.findings-naacl.21
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2024
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 287–301
- Language:
- URL:
- https://aclanthology.org/2024.findings-naacl.21
- DOI:
- Cite (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.
- Cite (Informal):
- Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks (Choenni et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2024.findings-naacl.21.pdf