Papers with prompt generator
SharPT: Shared Latent Space Prompt Tuning (2023.findings-eacl)
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| Challenge: | Prompt tuning is an efficient method for adapting large language models, but it is difficult and expensive to identify the source task that provides optimal prompts. |
| Approach: | They propose to learn a shared latent space which captures a set of basis skills from a mixture of source tasks and then transfer them to target tasks. |
| Outcome: | The proposed method outperforms previous methods on NLI, sentence completion, QA, conference resolution, word sense disambiguation and on various model scales. |
MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension (2023.findings-emnlp)
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| Challenge: | Existing soft prompt methods focus on designing the input-independent prompts that steer the model to fit the domain of the new dataset. |
| Approach: | They propose a multi-level prompt tuning method that utilizes prompts at task-specific, domain-specific and context-specific levels to enhance the comprehension of input semantics. |
| Outcome: | The proposed method improves on 12 benchmarks on various QA formats and achieves an average improvement of 1.94% over the state-of-the-art methods. |
CBP-Tuning: Efficient Local Customization for Black-box Large Language Models (2025.emnlp-main)
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| Challenge: | Customized black-box prompt tuning is a new approach to customize large language models . however, as models grow, the resources required for training and deployment become increasingly expensive . |
| Approach: | They propose a framework that facilitates efficient local customization while preserving bidirectional privacy. |
| Outcome: | The proposed framework facilitates efficient local customization while preserving bidirectional privacy. |
CAP: Controllable Alignment Prompting for Unlearning in LLMs (2026.acl-long)
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Zhaokun Wang, Jinyu Guo, Jingwen Pu, Hongli Pu, Meng Yang, Xunlei Chen, Jie Ou, Wenyi Li, Guangchun Luo, Wenhong Tian
| Challenge: | Existing methods for modifying parameters are unsystematic and rely on empirical experience. |
| Approach: | They propose a controllable alignment prompting for unlearning framework that decouples unlearning into a learnable prompt optimization process via reinforcement learning. |
| Outcome: | The proposed framework achieves precise, controllable unlearning without updating model parameters. |