Papers with prompt-based tuning framework
FPT: Feature Prompt Tuning for Few-shot Readability Assessment (2024.naacl-long)
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| Challenge: | Prompt-based methods lack crucial linguistic knowledge for readability assessment tasks such as word length, sentence length, and usage of different difficulty-level words. |
| Approach: | They propose a new prompt-based tuning framework that incorporates linguistic knowledge and a loss function to calibrate the similarity ranking order between categories. |
| Outcome: | The proposed framework outperforms the large language model gpt-3.5-turbo-16k in most cases. |
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. |