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arXiv AI By Kajetan Dymkiewicz, Ivan Vulic, Helen Yannakoudakis, Eilam Shapira, Roi Reichart, Anna Korhonen

Donors and Recipients: On Asymmetric Transfer Across Tasks and Languages with Parameter-Efficient Fine-Tuning

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The study investigates how fine‑tuning a large language model on a single task‑language pair influences performance on other task‑language pairs. Using LoRA fine‑tuning across multiple open‑weight LLM families, 11 languages, and four benchmarks, the authors decompose transfer into matched‑task, cross‑task, and cross‑task cross‑language regimes. They find that while single‑source fine‑tuning generally improves performance, the gains are highly asymmetric, with matched‑task cross‑language transfer being most effective and driven mainly by the target language rather than model architecture.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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