Papers with adapter-tuning
Inducer-tuning: Connecting Prefix-tuning and Adapter-tuning (2022.emnlp-main)
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| Challenge: | Prefix-tuning is an essential paradigm of parameter-efficient transfer learning . fine-tuned models require separate copies of model parameters for each task . |
| Approach: | They propose to understand and further develop prefix-tuning through the kernel lens . they propose a new variant of prefix tuning that shares the exact mechanism as prefix tun . |
| Outcome: | The proposed method improves prefix-tuning performance by training only a small portion of parameters. |
Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning (2023.emnlp-main)
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| Challenge: | Existing methods for fine-tuning pre-trained language models are limited to low-data regimes and require learning different modules to adapt to diverse tasks. |
| Approach: | They propose a framework for parameter-efficient fine-tuning that trains modules per task . they use an instance-dense retriever and a prototypical hypernetwork to generate conditional modules . |
| Outcome: | The proposed framework outperforms existing methods on multi-task learning and few-shot transfer learning. |
Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning (2022.findings-emnlp)
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| Challenge: | Existing approaches to transfer a pretrained language model include fine-tuning all the parameters in the language model and adapting all its subsets. |
| Approach: | They propose to select layers based on the variability of their hidden states given a task-specific corpus. |
| Outcome: | The proposed model reduces the computational cost of transfer learning methods without sacrificing performance. |
Not All Adapters Matter: Selective Adapter Freezing for Memory-Efficient Fine-Tuning of Language Models (2025.naacl-long)
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| Challenge: | Existing methods for fine-tuning pre-trained models impose substantial resource usage. |
| Approach: | They propose a parameter-efficient fine-tuning method that freezes adapters early to reduce resource usage while maintaining performance. |
| Outcome: | The proposed method reduces memory usage, computation amount, and training time by 42.85%, 34.59%, and 11.82% while maintaining performance. |
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks (2023.findings-acl)
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| Challenge: | Pretraining and fine-tuning are the dominant paradigms in natural language processing. |
| Approach: | They propose a parameter-efficient multitask learning framework that takes trainable hyper-embeddings and visual modality as input and outputs weights for different modules in a pretrained language model. |
| Outcome: | The proposed framework adds fewer trainable parameters in multi-task learning while achieving superior performances and transfer ability compared to state-of-the-art methods. |