mlsanityData to 14 Oct 2026

Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation

arXiv 2210.02952 ↗First posted 6 Oct 2022cs.CLcited by 15

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Abstract (arXiv)

Prompt tuning, or the conditioning of a frozen pretrained language model (PLM) with soft prompts learned from data, has demonstrated impressive performance on a wide range of NLP tasks. However, prompt tuning requires a large training dataset to be effective and is outperformed by finetuning the entire PLM in data-scarce regimes. Previous work (Gu et al., 2022, Vu et al., 2022) proposed to transfer soft prompts pretrained on the source domain to the target domain. In this paper, we explore domain adaptation for prompt tuning, a problem setting where unlabeled data from the target domain are available during pretraining. We propose bOosting Prompt TunIng with doMain Adaptation (OPTIMA), which regularizes the decision boundary to be smooth around regions where source and target data distributions are similar. Extensive experiments demonstrate that OPTIMA significantly enhances the transferability and sample-efficiency of prompt tuning compared to strong baselines. Moreover, in few-shot settings, OPTIMA exceeds full-model tuning by a large margin.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 52 of its 66 references that are in the map. First posted on arXiv 6 Oct 2022.

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52 of 66 references.

References · 52

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Citing papers per year: 2022: 1; 2023: 6; 2024: 5; 2025: 2; 2026: 1.

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  1. 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Surveycited by 135
  2. 2024Generative AI for Synthetic Data Generation: Methods, Challenges and the Futurecited by 33
  3. 2023Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learningcited by 26
  4. 2022On the Domain Adaptation and Generalization of Pretrained Language Models: A Surveycited by 23
  5. 2025CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Mergingcited by 23
  6. 2023Instruction Tuned Models are Quick Learnerscited by 21
  7. 2023Robust Prompt Optimization for Large Language Models Against Distribution Shiftscited by 19
  8. 2023Dynamic Prompting: A Unified Framework for Prompt Tuningcited by 12
  9. 2024Prompt-Based Bias Calibration for Better Zero/Few-Shot Learning of Language Modelscited by 8
  10. 2026From Weights to Activations: Is Steering the Next Frontier of Adaptation?cited by 8
  11. 2024Concentrate Attention: Towards Domain-Generalizable Prompt Optimization for Language Modelscited by 7
  12. 2023Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognitioncited by 5
3 more
  1. 2024How Useful is Continued Pre-Training for Generative Unsupervised Domain Adaptation?cited by 5
  2. 2024Generating Synthetic Datasets for Few-shot Prompt Tuningcited by 1
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