Papers with prompt-based tuning
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study (2022.findings-emnlp)
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| Challenge: | Existing approaches to extract relational facts from text are limited in their ability to learn from limited labeled data. |
| Approach: | They propose to use prompt-based methods with few-shot labeled data to evaluate performance . data augmentation technologies and self-training are also proposed to generate more labeles in-domain data. |
| Outcome: | The proposed methods perform well in low-resource settings with 8 relation extraction datasets. |
PromptDA: Label-guided Data Augmentation for Prompt-based Few Shot Learners (2023.eacl-main)
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| Challenge: | Existing studies on prompt-based few-shot tuning focus on deriving proper label words with a verbalizer or generating prompt templates to elicit semantics from PLMs. |
| Approach: | They propose a framework that leverages label semantics for prompt-based tuning. |
| Outcome: | The proposed framework improves on few-shot text classification tasks by leveraging label semantics and data augmentation. |
PrAd: Prompt Adaptive Tuning for Decoder-only Language Models (2025.findings-emnlp)
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| Challenge: | Prompt-based methods suffer from increased input lengths and sensitivity to weight initialization . adapter-based approaches can substantially increase inference time . |
| Approach: | a new paradigm for prompt-based tuning addresses the problem of fine tuning pretrained models . prompt--based methods suffer from increased input lengths and sensitivity to weight initialization . a prompt-oriented approach employs adapters for flexible input transformation . |
| Outcome: | a proposed framework can achieve comparable or better performance and higher inference efficiency even in multi-task scenarios. |
Prototypical Verbalizer for Prompt-based Few-shot Tuning (2022.acl-long)
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| Challenge: | Prompt-based tuning for pre-trained language models has shown its effectiveness in few-shot learning. |
| Approach: | They propose a prototypical verbalizer which learns prototype vectors as verbalizes by contrastive learning. |
| Outcome: | The proposed verbalizer outperforms existing verbalizing methods on topic classification and entity typing tasks. |