Papers with Prompt-tuning
Prompt-Based Meta-Learning For Few-shot Text Classification (2022.emnlp-main)
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| Challenge: | Existing methods to learn text labels require large amounts of data to build many few-shot tasks. |
| Approach: | They propose a Prompt-Based Meta-Learning model that adds the prompting mechanism to the meta-learning method. |
| Outcome: | The proposed method improves on four text classification datasets with high accuracy and robustness. |
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. |
Making Pretrained Language Models Good Long-tailed Learners (2022.emnlp-main)
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| Challenge: | Prompt-tuning has shown appealing performance in few-shot classification . however, it is less promising in long-tailed classification due to long tail . |
| Approach: | They propose to use prompt-tuning to make pretrained language models at least good long-tailed learners by bridging the gap between prompt- and commonly used finetun. |
| Outcome: | The proposed method makes pretrained language models at least good long-tailed learners, bridging the gap between prompt-tuning and finetunation. |
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing (2022.emnlp-main)
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Linlu Qiu, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, Kristina Toutanova
| Challenge: | Pre-trained language models struggle on out-of-distribution compositional generalization . recent work shows considerable improvements on many NLP tasks from model scaling . |
| Approach: | They evaluate encoder-decoder models up to 11B parameters and decoder-only models up 540B parameters . they compare scaling curves for fine-tuning, prompt tuning, and in-context learning methods . |
| Outcome: | The proposed scaling methods improve compositional generalization on many tasks . fine-tuning generally has flat or negative scaling curves on out-of-distribution compositional . larger models are better at modeling the syntax of the output space, the study finds . |