Typologically Informed Parameter Aggregation

• URL: http://arxiv.org/abs/2601.16629v1
• Date: Fri, 23 Jan 2026 10:32:33 GMT
• Title: Typologically Informed Parameter Aggregation
• Authors: Stef Accou, Wessel Poelman,
• Abstract summary: Massively multilingual language models enable cross-lingual generalization but underperform on low-resource and unseen languages.<n>We introduce Typologically Informed Aggregation (TIPA), a training-free method that constructs proxy language adapters by aggregating existing ones.<n> Integrated into the MAD-X framework, these proxies enable zero-shot cross-lingual transfer without additional training.
• Score: 0.27930955543692815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Massively multilingual language models enable cross-lingual generalization but underperform on low-resource and unseen languages. While adapter-based fine-tuning offers a parameter-efficient solution, training language-specific adapters at scale remains costly. We introduce Typologically Informed Parameter Aggregation (TIPA), a training-free method that constructs proxy language adapters by aggregating existing ones, weighted by typological similarity. Integrated into the MAD-X framework, these proxies enable zero-shot cross-lingual transfer without additional training. We evaluate TIPA on five NLP tasks and over 230 languages. TIPA consistently outperforms or matches baselines such as English-only fine-tuning or selecting the typologically closest language adapter. We see the largest gains for languages lacking dedicated adapters. Our results demonstrate that typologically informed aggregation provides a viable alternative to language-specific modules without any training needed.

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