DeFT-X: Denoised Sparse Fine-Tuning for Zero-Shot Cross-Lingual Transfer (2025.findings-emnlp)
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| Challenge: | Prior studies have explored multiple approaches to combine task knowledge from task-specific data in a (high-resource) source language with language knowledge from unlabeled text in 'low-resourced' target language. |
| Approach: | They propose a composable sparse fine-tuning approach that learns task-specific and language-specific sparsen masks to select a subset of the pretrained model's parameters. |
| Outcome: | The proposed approach performs at par or outperforms SFT and other prominent cross-lingual transfer baselines. |
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| Challenge: | Adapters and sparse fine-tuning have been developed to improve transfer learning . a number of approaches have been proposed to improve performance of fine-untuners . |
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| Challenge: | Recent work highlights the fallacies of zero-shot cross-lingual transfer with large multilingual models. |
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| Challenge: | Existing methods focus on layer-selective and data-selectory fine-tuning, but ignore the fact that different data points contribute varying degrees to distinct model layers. |
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Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
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Unifying Cross-Lingual Transfer across Scenarios of Resource Scarcity (2023.emnlp-main)
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| Challenge: | Existing approaches to deal with resource scarcity have not been developed to deal effectively with the problem. |
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| Challenge: | Recent advances in language modelling have led to the availability of many pre-trained language models (PLMs); however, how much data is needed to fine-tune PLMs for downstream tasks? |
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| Challenge: | Adapting large language models to other languages often suffers from an overemphasis on English performance. |
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FAD-X: Fusing Adapters for Cross-lingual Transfer to Low-Resource Languages (2022.aacl-short)
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| Challenge: | Adapter-based tuning is a technique that selectively updates language-specific parameters to adapt to a new language, rather than fine-tuning all shared weights. |
| Approach: | They propose to add light-weight adapters to multilingual pretrained language models (mPLMs) and add language-specific parameters to adapt to a new language. |
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Cross-Lingual Transfer with Target Language-Ready Task Adapters (2023.findings-acl)
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| Challenge: | Existing frameworks for (zero-shot) cross-lingual transfer employ separate language and task adapters which can be arbitrarily combined to perform any task to any target language. |
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