| Challenge: | Existing prompt tuning methods use a fixed prompt in each input instance during the model training stage. |
| Approach: | They propose a conditional prompt generation method to generate prompts for each input instance. |
| Outcome: | The proposed method outperforms other prompt tuning methods while tuning fewer parameters. |
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Attribute Controlled Dialogue Prompting (2023.findings-acl)
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| Challenge: | Prompt-tuning is an increasingly popular parameter-efficient method for adapting large pretrained language models to downstream tasks. |
| Approach: | They propose an instance-specific prompt-tuning algorithm for dialog generation that generates prompts based on instance-level control code rather than the conversation history. |
| Outcome: | The proposed prompt-tuning module is a fraction of the size of the pretrained language model and saves memory and expensive storage space. |
IAPT: Instance-Aware Prompt Tuning for Large Language Models (2024.acl-long)
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| Challenge: | Existing methods for prompt tuning require many soft tokens to guarantee performance . large language models still require a large amount of GPU memory and computations to fine-tune . |
| Approach: | They propose to use a parameter-efficient soft prompt generator to generate idiosyncratic soft prompts for each input instruction. |
| Outcome: | The proposed method outperforms the baselines with comparable tunable parameters and is more efficient than LoRA under the single-backbone multi-tenant setting. |
Learning to Transfer Prompts for Text Generation (2022.naacl-main)
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| Challenge: | Pretrained language models (PLMs) have made remarkable progress in text generation tasks via fine-tuning. |
| Approach: | They propose a prompt-based method that learns source prompts and transfers them as target prompts to perform target generation tasks. |
| Outcome: | The proposed method can be used to perform text generation tasks in a transferable setting. |
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. |
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. |
PromptGen: Automatically Generate Prompts using Generative Models (2022.findings-naacl)
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| Challenge: | Recent prompt learning has received significant attention, where downstream tasks are reformulated to the mask-filling task with the help of a textual prompt. |
| Approach: | They propose a model PromptGen which can automatically generate prompts conditional on the input sentence. |
| Outcome: | The proposed model outperforms baseline models on the knowledge probing LAMA benchmark. |
Discourse-Aware Soft Prompting for Text Generation (2022.emnlp-main)
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| Challenge: | Recent advances in pre-trained langauge models (PLMs) have made great impact on text generation research. |
| Approach: | They propose to use hierarchical blocking to simulate a higher-level discourse structure of human written text and attention sparsity to learn sparse transformations on the softmax-function. |
| Outcome: | The proposed methods perform better on some generation tasks but don't generalize across all generation tasks. |
How Does In-Context Learning Help Prompt Tuning? (2024.findings-eacl)
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| Challenge: | a growing number of parameter-efficient adaptation methods are needed to fine-tune large language models. |
| Approach: | They propose a method that combines prompt tuning and in-context learning to improve prompt tuning by concatenating a natural language demonstration with learned prompt embeddings. |
| Outcome: | The proposed method outperforms prompt tuning and prompt tuning on five language generation tasks. |
The Power of Prompt Tuning for Low-Resource Semantic Parsing (2022.acl-short)
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| Challenge: | Prompt tuning is an effective method for adapting pre-trained language models to downstream tasks. |
| Approach: | They propose to use prompt tuning for semantic parsing to map natural language utterances onto formal meaning representations. |
| Outcome: | The proposed method outperforms the fine-tuned model on low-resource splits of Overnight and TOPv2 on language representations with increasing model scale and target representations. |
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. |