Mitigate Negative Transfer with Similarity Heuristic Lifelong Prompt Tuning (2024.findings-acl)
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| Challenge: | Existing approaches to improve the performance of lifelong prompt tuning are limited due to inherent challenges. |
| Approach: | They propose a similarity heuristic lifelong prompt tuning framework that partitions tasks into two distinct subsets by harnessing a learnable similarity measure. |
| Outcome: | The proposed framework outperforms state-of-the-art techniques in lifelong learning benchmarks and demonstrates robustness against negative transfer in diverse task sequences. |
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Prompts Can Play Lottery Tickets Well: Achieving Lifelong Information Extraction via Lottery Prompt Tuning (2023.acl-long)
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| Challenge: | Existing research on information extraction tasks focuses on one specific task, but in real-world scenarios, new data of different IE tasks and domains come in a stream over time. |
| Approach: | They propose a parameter- and deployment-efficient prompt tuning method to evaluate the UIE system under a “lifelong learning” setting. |
| Outcome: | The proposed method is able to learn new tasks without forgetting old ones and expand knowledge and functionalities without retraining the whole system. |
Q-Tuning: Queue-based Prompt Tuning for Lifelong Few-shot Language Learning (2024.findings-naacl)
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| Challenge: | Existing methods for continual prompt tuning are limited by the ever-growing parameter scale of modern language models (e.g., GPT-4 that may have 1.76 trillion parameters). |
| Approach: | They propose a method for continual prompt tuning that enables the lifelong learning of a pre-trained language model by adding a task-specific prompt to a queue of older tasks. |
| Outcome: | The proposed method outperforms the state-of-the-art methods substantially on continual prompt tuning benchmarks. |
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer (2022.acl-long)
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| Challenge: | Recent studies show that pre-trained language models can be more efficient when they are larger than they are in their size. |
| Approach: | They propose a prompt-based transfer learning approach called SPoT: Soft Prompt Transfer that learns a soft prompt on one or more source tasks and initializes it for a target task. |
| Outcome: | The proposed approach outperforms Prompt Tuning and MODELTUNING on superGLUE benchmarks while using up to 27,000 fewer task-specific parameters. |
Parameter Efficient Multi-task Fine-tuning by Learning to Transfer Token-wise Prompts (2023.findings-emnlp)
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Muling Wu, Wenhao Liu, Jianhan Xu, Changze Lv, Zixuan Ling, Tianlong Li, Longtao Huang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Prompt tuning has been proven to be successful on various tasks by incorporating a small number of trainable parameters while freezing large pre-trained language models. |
| Approach: | They propose a token-wise prompt tuning method that uses a bank of finer-grained soft prompt tokens to generate an instance-dependent prompt. |
| Outcome: | The proposed method performs far better than full parameter fine-tuned models and achieves state-of-the-art by tuning only 0.035% parameters on 14 datasets. |
The Power of Scale for Parameter-Efficient Prompt Tuning (2021.emnlp-main)
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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. |
Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning (2023.findings-emnlp)
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| Challenge: | Large-scale pre-trained language models have been fine-tuned for various NLP tasks . prompt tuning is a method that optimizes the output of the model to adapt to downstream tasks based on the posterior distribution of the source task. |
| Approach: | They propose a Bayesian approach to prompt tuning that optimizes for adapting pre-trained language models to downstream tasks rather than fine-tuning full model parameters. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on benchmark NLP tasks. |
SPT: Learning to Selectively Insert Prompts for Better Prompt Tuning (2023.emnlp-main)
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| Challenge: | Prompt tuning is a method of pre-trained models that optimizes the prompt to adapt to downstream tasks. |
| Approach: | They propose a framework that learns to select the proper prompt layers by inserting a probabilistic gate at each intermediate layer. |
| Outcome: | The proposed framework can perform better than the state-of-the-art prompt tuning frameworks on ten benchmark datasets. |
Is Prompt Transfer Always Effective? An Empirical Study of Prompt Transfer for Question Answering (2024.naacl-short)
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| Challenge: | Prompt tuning is an efficient method for initializing pre-trained models . but initialization of prompts is sensitive when the model size is small . |
| Approach: | They propose a method to measure catastrophic forgetting by analyzing prompts for the first time . they characterize a question answering task based on answer format and prompt initialization . |
| Outcome: | The proposed approach can help deepen understanding of prompt tuning. |
On Transferability of Prompt Tuning for Natural Language Processing (2022.naacl-main)
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Yusheng Su, Xiaozhi Wang, Yujia Qin, Chi-Min Chan, Yankai Lin, Huadong Wang, Kaiyue Wen, Zhiyuan Liu, Peng Li, Juanzi Li, Lei Hou, Maosong Sun, Jie Zhou
| Challenge: | Pre-trained language models (PLMs) can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but require much more training time than fine-timing. |
| Approach: | They empirically investigate the transferability of soft prompts across different downstream tasks and PLMs to determine what decides prompt transferability. |
| Outcome: | The proposed method can achieve comparable performance to full-parameter fine-tuning by tuning a few soft prompts, but requires much more training time than fine-timing. |
Continual Prompt Tuning for Dialog State Tracking (2022.acl-long)
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| Challenge: | Existing methods to train a model on a sequence of tasks are not efficient enough to mitigate catastrophic forgetting. |
| Approach: | They propose a parameter-efficient framework that prevents forgetting and enables knowledge transfer between tasks by learning and freezing a pre-trained model. |
| Outcome: | The proposed framework avoids forgetting and enables knowledge transfer between tasks. |