Papers by Byung-Gon Chun
Two Examples are Better than One: Context Regularization for Gradient-based Prompt Tuning (2023.findings-acl)
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| Challenge: | Prompting has gained tremendous attention as an efficient method for the adaptation of large-scale language models. |
| Approach: | They propose a regularization method that guides a prompt to produce a task context properly. |
| Outcome: | The proposed method improves prediction performance in a zero-shot in-context learning setting without demonstration examples for in-constitu learning. |
Meta-Learning of Prompt Generation for Lightweight Prompt Engineering on Language-Model-as-a-Service (2023.findings-emnlp)
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| Challenge: | Language-Model-as-a-Services (LMaaSs) support a variety of user tasks through in-context learning from prompts. |
| Approach: | They propose a lightweight automatic prompt generation method that meta-trains a prompt generation model to enable robust learning from the contexts created by the generated prompts. |
| Outcome: | The proposed method improves performance on unseen tasks by 19.4% compared to the state-of-the-art prompt generation method. |