Papers by Sangmin Bae
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. |
Carpe diem: On the Evaluation of World Knowledge in Lifelong Language Models (2024.naacl-long)
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| Challenge: | Current language models are trained on static data, implying that the encoded knowledge could go wrong as time passes. |
| Approach: | They propose a temporally evolving question-answering benchmark for language models . they use Wikipedia databases to test language models for dynamic knowledge in ever-changing world . |
| Outcome: | The proposed task aims to model the evolution-adaptability of language models in the real world. |
Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding (2023.emnlp-main)
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| Challenge: | Existing approaches to speed up inference latency have shown performance degradation caused by a state copying mechanism or numerous exit paths. |
| Approach: | They propose a framework that allocates adaptive computation paths for each token based on the complexity of generating the subsequent token. |
| Outcome: | The proposed framework outperforms existing frameworks on extensive generation tasks. |