Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide (2026.tacl-1)
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| Challenge: | Pre-trained language models provide strong foundations, but effective adaptation under data scarcity requires efficient and efficient fine-tuning techniques. |
| Approach: | They propose to review parameter-efficient fine-tuning techniques that lower training and deployment costs and domain and cross-lingual adaptation methods for both encoder and decoder models. |
| Outcome: | The proposed techniques lower training and deployment costs, domain and cross-lingual adaptation methods, and model specialization strategies. |
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| Challenge: | Existing studies focus on data selection but lack a clear, unified framework . variability in experimental settings complicates systematic comparisons . |
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| Challenge: | Traditionally, success in multilingual machine translation depends on large volume, diverse directions, and high quality of training data. |
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| Challenge: | Existing LLMs mainly support English alongside a handful of high resource languages . this leaves a major gap for most low-resource languages despite increasing pace of research . |
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Shenglai Zeng, Yaxin Li, Jie Ren, Yiding Liu, Han Xu, Pengfei He, Yue Xing, Shuaiqiang Wang, Jiliang Tang, Dawei Yin
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The Effect of Language Diversity When Fine-Tuning Large Language Models for Translation (2025.findings-emnlp)
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| Challenge: | Prior research on language diversity in LLM fine-tuning has reported benefits while others find no benefits. |
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