基于主题约束采样的文本生成方法OA

Pre-trained large language models can be fine-tuned to incorporate relevant knowledge and linguistic conven-tions of a local corpus,though this process typically involves high training costs and computing resources.Therefore,this paper proposes a sampling method based on topic-constrained.The method employs a local corpus to construct a latent Dirichlet distribution(LDA)topic model.LDA is used to topic-constrained the output content.On the one hand,this method is easy to implement.On the other hand,it enhances the generalization capabilities of model on local corpus.Experimental results reveal that the proposed method achieves better performance than the baseline model in both diversity and generalization.