Papers by Minchan Jeong
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL (2024.acl-long)
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Yunseon Choi, Sangmin Bae, Seonghyun Ban, Minchan Jeong, Chuheng Zhang, Lei Song, Li Zhao, Jiang Bian, Kee-Eung Kim
| Challenge: | Prompt tuning is an important technique for directing model behaviors and eliciting desired responses. |
| Approach: | They propose to find optimal prompt tokens using soft Q-learning to optimize models for prompt tuning. |
| Outcome: | The proposed method improves on baseline prompt tuning, and the results are more natural and interpretable. |
Revisiting Intermediate Layer Distillation for Compressing Language Models: An Overfitting Perspective (2023.findings-eacl)
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| Challenge: | Existing methods for knowledge distillation (KD) are prone to overfitting to training datasets . recent advances in NLP have shown that using PLMs such as BERT and RoBERTa on downstream tasks is effective. |
| Approach: | They propose a consistency-regularized knowledge distillation method which mitigates overfitting of existing methods. |
| Outcome: | The proposed method outperforms existing methods on the GLUE benchmark and synthetic datasets. |
Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning (2023.findings-emnlp)
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| Challenge: | Large-scale pre-trained language models have been fine-tuned for various NLP tasks . prompt tuning is a method that optimizes the output of the model to adapt to downstream tasks based on the posterior distribution of the source task. |
| Approach: | They propose a Bayesian approach to prompt tuning that optimizes for adapting pre-trained language models to downstream tasks rather than fine-tuning full model parameters. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on benchmark NLP tasks. |
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization (2024.findings-emnlp)
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| Challenge: | Existing approaches to align Large Language Models with human preferences fail to maintain general knowledge and alignment when faced with personalized preferences. |
| Approach: | They propose a method that utilizes the initial responses of the reference model to mitigate forgetting while accommodating personalized alignment. |
| Outcome: | The proposed approach mitigates forgetting while accommodating personalized alignment while preserving global knowledge and general alignment. |