Papers by Congda Ma
Focused Prefix Tuning for Controllable Text Generation (2023.acl-short)
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| Challenge: | Existing unannotated attributes could degrade models' performance . focus on the desired attribute can be achieved with focused prefix tuning . |
| Approach: | They propose focused prefix tuning to enable the control to focus on the desired attribute . they propose to reduce the number of unannotated attributes in a controllable text generation dataset . |
| Outcome: | The proposed approach achieves better control accuracy and text fluency than baseline models in single-attribute tasks. |
Debiasing Large Language Models with Structured Knowledge (2024.findings-acl)
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| Challenge: | Existing methods to reduce biases in pre-training models are hampered by their performance. |
| Approach: | They propose a method that utilizes structured knowledge to mitigate bias in LLMs . their method obviates the need for training from scratch, thus offering enhanced scalability . |
| Outcome: | The proposed method outperforms state-of-the-art (SOTA) baselines in the debiasing ability. |