Residual Prompt Tuning: improving prompt tuning with residual reparameterization (2023.findings-acl)
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| Challenge: | Prompt tuning is one of the most parameter-efficient approaches for parameter-effective tuning of pre-trained language models. |
| Approach: | They propose to reparameterize soft prompt embeddings using a shallow network with a residual connection and use it to tune prompt embeds P. |
| Outcome: | The proposed method outperforms prompt tuning on SuperGLUE, T5-Base and BERT-Bass models and can reduce the prompt length by 10 times without hurting performance. |
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| Challenge: | Unlike discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signals from any number of labeled examples. |
| Approach: | They propose a mechanism for learning "soft prompts" to condition frozen language models to perform specific downstream tasks. |
| Outcome: | The proposed method outperforms fewshot learning using GPT-3 and matches the quality of model tuning as models exceed billions of parameters. |
Decomposed Prompt Tuning via Low-Rank Reparameterization (2023.findings-emnlp)
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| Challenge: | Pre-trained language models have achieved remarkable performance on various tasks. |
| Approach: | They propose a decomposed prompt tuning approach that utilizes low-rank matrices to initialize the soft prompt. |
| Outcome: | The proposed method significantly reduces the number of trainable parameters while maintaining effectiveness. |
PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer (2023.emnlp-main)
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| Challenge: | Existing prompt tuning methods have training instability issues due to large variance of scores . existing prompt tuning algorithms have training stability issues due a slight change of input data . |
| Approach: | They propose an algorithm that smooths the loss landscape of vanilla prompt tuning by perturbation-based regularizers. |
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Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL (2024.acl-long)
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Yunseon Choi, Sangmin Bae, Seonghyun Ban, Minchan Jeong, Chuheng Zhang, Lei Song, Li Zhao, Jiang Bian, Kee-Eung Kim
| Challenge: | Prompt tuning is an important technique for directing model behaviors and eliciting desired responses. |
| Approach: | They propose to find optimal prompt tokens using soft Q-learning to optimize models for prompt tuning. |
| Outcome: | The proposed method improves on baseline prompt tuning, and the results are more natural and interpretable. |
P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks (2022.acl-short)
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| Challenge: | Existing methods of prompt tuning cannot handle hard sequence labeling tasks. |
| Approach: | They propose to optimize prompt tuning to tune continuous prompts with a frozen language model. |
| Outcome: | The proposed method matches finetuning with prompt tuning while having only 0.1%-3% tuned parameters. |
SMoP: Towards Efficient and Effective Prompt Tuning with Sparse Mixture-of-Prompts (2023.emnlp-main)
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| Challenge: | Prompt tuning has emerged as a successful parameter-efficient alternative to the full fine-tuning of language models. |
| Approach: | They propose a prompt tuning method that utilizes short soft prompts for efficient training and inference while maintaining performance gains typically induced by longer soft prompt. |
| Outcome: | The proposed method outperforms baseline methods while preserving memory usage. |
Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers (2023.findings-emnlp)
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| Challenge: | Prompt tuning is a technique that updates few parameters in pre-trained models for language understanding and generation tasks. |
| Approach: | They propose to leverage prompt tuning for neural text retrieval to improve generalization and cross-domain generalization. |
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Context-Tuning: Learning Contextualized Prompts for Natural Language Generation (2022.coling-1)
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| Challenge: | Recent studies have shown that pretrained language models (PLMs) lack sufficient consideration of input semantics to generate natural language. |
| Approach: | They propose a continuous prompting approach to fine-tune PLMs for natural language generation by modeling an inverse generation process from output to input. |
| Outcome: | The proposed method fine-tunes only 0.12% of the parameters while maintaining good performance. |
Prompt Tuning for Unified Multimodal Pretrained Models (2023.findings-acl)
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| Challenge: | Prompt tuning has demonstrated success in natural language pretraining and even vision pretraining. |
| Approach: | They propose to apply prompt tuning to a unified sequence-to-sequence pretrained model by adding a sequence of learnable embeddings to each layer and finetuning the pretrained models on downstream tasks. |
| Outcome: | The proposed method outperforms other parameter-efficient tuning methods on multimodal models and is robust against adversarial attacks. |
Late Prompt Tuning: A Late Prompt Could Be Better Than Many Prompts (2022.findings-emnlp)
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| Challenge: | Prompt tuning is parameter-efficient but lags behind other state-of-the-art methods. |
| Approach: | They propose a parameter-efficient tuning method that only optimizes a soft prompt to adapt PTMs to downstream tasks. |
| Outcome: | The proposed method is parameter-efficient but lags behind other state-of-the-art methods. |