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Empirical analysis indicates that complex LoRA variants offer no significant performance advantage over basic LoRA for multilingual instruction tuning, as they fail to improve cross-lingual transfer or knowledge retention. Hidden embedding analysis further suggests that architectural novelty in these techniques does not translate into better language representation layers. For practitioners, this finding supports prioritizing simpler, more efficient fine-tuning configurations without sacrificing multilingual capabilities.
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Which LoRA? An Empirical Study on the Effectiveness of LoRA Techniques During Multilingual Instruction Tuning

arXiv2 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2606.10428v1 Announce Type: new Abstract: We investigate whether commonly available LoRA variants have an advantage over basic LoRA in multilingual instruction tuning. Experiments involving LoRA and four other variants on two datasets across diverse target languages show that there is no significant advantage in using more complex LoRA variants instead of basic LoRA, with respect to balancing cross-lingual transfer and knowledge retention. An analysis of hidden embeddings reveal that layer-wise language representation remains largely similar across LLMs fine-tuned with different LoRA techniques, suggesting that architectural novelty of LoRA techniques may not translate into better cross-lingual adaptation.

Title:Which LoRA? An Empirical Study on the Effectiveness of LoRA Techniques During Multilingual Instruction Tuning

Abstract:We investigate whether commonly available LoRA variants have an advantage over basic LoRA in multilingual instruction tuning. Experiments involving LoRA and four other variants on two datasets across diverse target languages show that there is no significant advantage in using more complex LoRA variants instead of basic LoRA, with respect to balancing cross-lingual transfer and knowledge retention. An analysis of hidden embeddings reveal that layer-wise language representation remains largely similar across LLMs fine-tuned with different LoRA techniques, suggesting that architectural novelty of LoRA techniques may not translate into better cross-lingual adaptation.

Computation and Language (cs.CL)

arXiv:2606.10428 [cs.CL]

(or arXiv:2606.10428v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.10428 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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