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

arXiv 2606.10428 ↗First posted 9 Jun 2026cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 9 Jun 2026.

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References · 11

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2018XNLI: Evaluating Cross-lingual Sentence Representationscited by 915
  3. 2023VeRA: Vector-based Random Matrix Adaptationcited by 300
  4. 2024Do Llamas Work in English? On the Latent Language of Multilingual Transformerscited by 281
  5. 2024Multilingual Instruction Tuning With Just a Pinch of Multilingualitycited by 73
  6. 2025Language Arithmetics: Towards Systematic Language Neuron Identification and Manipulationcited by 17
  7. 2024Challenges in Adapting Multilingual LLMs to Low-Resource Languages using LoRA PEFT Tuningcited by 14
  8. 2023Low-Rank Adaptation for Multilingual Summarization: An Empirical Studycited by 13
  9. 2025Continually Adding New Languages to Multilingual Language Modelscited by 7
  10. 2026LinguaMap: Which Layers of LLMs Speak Your Language and How to Tune Them?cited by 3
  11. 2025What Causes Knowledge Loss in Multilingual Language Models?cited by 2

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