1. 融合对抗训练的中文GPT 对话模型研究.
- Author
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王 伟, 阮文翰, and 孟祥福
- Abstract
Pre-training in the cleaned Chinese conversation dataset have problems of reduced generalization ability of the conversation model and low evaluation index after fine-tuning. The method of confrontation training is used to propose a Chinese GPT dialogue model incorporating adversarial training. The projection gradient descent method is used for training during the fine-tuning process, and then the Focal loss function is used to speed up the training. The experimental results show that after integrating confrontation training, fine-tuning and testing in the noisy data set, the model reduces the interference factors such as noise. The experimental results show that after the fusion of adversarial training, the model has improved performance compared with the baseline model, and has strong anti-interference and generalization capabilities. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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