Papers with sparse fine-tuning
Examining Modularity in Multilingual LMs via Language-Specialized Subnetworks (2024.findings-naacl)
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| Challenge: | Recent work has proposed explicitly inducing language-wise modularity in multilingual LMs via sparse fine-tuning (SFT) on per-language subnetworks as a means of better guiding cross-lingual sharing. |
| Approach: | They propose to explicitly inducing language-wise modularity in multilingual LMs via sparse fine-tuning on per-language subnetworks to better guide cross-lingual sharing. |
| Outcome: | The proposed approach can increase language specialization of subnetworks in favor of more cross-lingual sharing. |
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. |
Parameter-Efficient Fine-Tuning without Introducing New Latency (2023.acl-long)
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| Challenge: | Parameter-efficient fine-tuning of pre-trained language models has been demonstrated to be effective, but its inherent characteristics limit its performance. |
| Approach: | They propose to generate a sparse mask in a task-agnostic manner by modifying only a small subset of existing parameters and adding new parameters. |
| Outcome: | The proposed method surpasses existing methods on the GLUE benchmark by a significant margin. |
Safety-Utility Conflicts Are Not Global: Surgical Alignment via Head-Level Diagnosis (2026.acl-long)
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| Challenge: | Existing mitigation strategies rely on global gradient geometry to resolve alignment conflicts . however, they overlook Modular Heterogeneity within Transformers, resulting in suboptimal trade-offs . Conflict-Aware Sparse Tuning (CAST) combines head-level diagnosis with sparse fine-tuning . |
| Approach: | They propose a framework that integrates head-level diagnosis with sparse fine-tuning to address this limitation. |
| Outcome: | The proposed framework integrates head-level diagnosis with sparse fine-tuning to reduce alignment conflicts in LLMs. |