Papers by Mingkai Deng
RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning (2022.emnlp-main)
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Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric Xing, Zhiting Hu
| Challenge: | Existing methods for finding the optimal prompt for a task are difficult to optimize. |
| Approach: | They propose an efficient discrete prompt optimization approach with reinforcement learning that generates the optimal discrete stimulus after training with reward. |
| Outcome: | The proposed approach is based on a parameter-efficient policy network that generates the optimal discrete prompt after training with reward. |
Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation (2021.emnlp-main)
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| Challenge: | Natural language generation (NLG) tasks have complex nature and require manual evaluation. |
| Approach: | They propose a unifying perspective based on the nature of information change in NLG tasks . they propose 'information alignment' metrics that can be used to evaluate different aspects of NLG . |
| Outcome: | The proposed metrics achieve stronger or comparable correlations with human judgement compared to state-of-the-art metrics in diverse tasks. |