Efficient Low-Resource Language Adaptation via Multi-Source Dynamic Logit Fusion

April 20, 2026 ยท Grace Period ยท ๐Ÿ› ACL 2026

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Authors Chen Zhang, Jiuheng Lin, Zhiyuan Liao, Yansong Feng arXiv ID 2604.18106 Category cs.CL: Computation & Language Citations 0 Venue ACL 2026
Abstract
Adapting large language models (LLMs) to low-resource languages (LRLs) is constrained by the scarcity of task data and computational resources. Although Proxy Tuning offers a logit-level strategy for introducing scaling effects, it often fails in LRL settings because the large model's weak LRL competence might overwhelm the knowledge of specialized smaller models. We thus propose TriMix, a test-time logit fusion framework that dynamically balances capabilities from three different sources: LRL competence from a continually pretrained small model, task competence from high-resource language instruction tuning, and the scaling benefits of large models. It is data- and compute-efficient, requiring no LRL task annotations, and only continual pretraining on a small model. Experiments across four model families and eight LRLs show that TriMix consistently outperforms single-model baselines and Proxy Tuning. Our analysis reveals that prioritizing the small LRL-specialized model's logits is crucial for success, challenging the prevalent large-model-dominant assumption.
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