Language model training method and apparatus based on continual pre-training

A computer-implemented method for fine-tuning a language processing model, wherein the language processing model comprises a fine-tuning soft prompt generation model, a fine-tuning language model, and a current predictive model, and the computer-implemented method comprises:

• iteratively performing the following model fine-tuning process by using a fine-tuning training sample set, until a fine-tuning termination condition is satisfied, wherein each training sample in the fine-tuning training sample set comprises text data and labeled data related to a fine-tuning task:
• providing a textual latent feature corresponding to each piece of text data in a current fine-tuning training sample set to a current fine-tuning soft prompt generation model to obtain a fine-tuning soft prompt feature corresponding to each current training sample;
• providing each piece of text data in the current fine-tuning training sample set and a corresponding fine-tuning soft prompt feature to a current fine-tuning language model to obtain a fine-tuning latent feature corresponding to each piece of text data, wherein an initial current fine-tuning soft prompt generation model and an initial current fine-tuning language model;
• providing the fine-tuning latent feature corresponding to each piece of text data to a current predictive model related to the fine-tuning task to obtain a prediction result corresponding to each piece of text data;
• determining a predicted loss value of a current model fine-tuning process based on a difference between the prediction result corresponding to each piece of text data and the labeled data;
• and
• in response to failure to satisfy the fine-tuning termination condition, adjusting model parameters of the current fine-tuning language model and the current predictive model based on the predicted loss value, wherein the current fine-tuning language model and the current predictive model after model parameter adjustment serve as a current fine-tuning language model and a current predictive model for a next model training process.

An apparatus comprising:

• one or more processors;
• and
• one or more tangible, non-transitory, machine-readable media storing one or more instructions that, when executed by the one or more processors, perform operations comprising a language model training method comprising:
• iteratively performing a model training process by using a training sample set in a current domain, until a training termination condition for the current domain is satisfied, wherein each training sample in the training sample set comprises text data:
• providing a textual latent feature corresponding to each piece of text data in a current training sample set in the current domain to a current soft prompt generation model to obtain a soft prompt feature corresponding to each current training sample;
• providing each piece of text data in the current training sample set and a corresponding soft prompt feature to a current language model to obtain a latent feature corresponding to each piece of text data in the current domain, wherein an initial current language model is obtained through training based on a training sample set in a previous domain;
• determining a cross-domain loss value based on a difference between the obtained latent feature corresponding to each piece of text data in the current domain and a corresponding latent feature that is obtained based on the initial current language model in the previous domain;
• in response to failure to satisfy the training termination condition for the current domain, adjusting model parameters of the current soft prompt generation model and the current language model based on the cross-domain loss value, wherein the current soft prompt generation model and the current language model after model parameter adjustment serve as a current soft prompt generation model and a current language model for a next model training process;
• and
• in response to satisfaction of the training termination condition for the current domain, continuing using a training sample set in a next domain to repeat the model training process by using the training sample set in the next domain, until a training termination condition for continual pre-training is satisfied.