Abstract: A data processing method is provided, applied to the field of text generation. The method includes: obtaining a first prompt; obtaining a first output by using a first LLM based on the first prompt; constructing a second prompt based on the first output and the first prompt, where the second prompt indicates to improve the first output when the first prompt is determined as a prompt; and obtaining a second output by using the first LLM based on the second prompt, where the second output is an improved first output. In this disclosure, self-learning and evolution can be implemented based on an output self-correction process of the LLM without completely depending on human supervision, or a more accurate reply can be obtained for a highly complex task.

Abstract: The human-computer dialogue method includes: obtaining second historical dialogue data, current dialogue environment data, and second input dialogue data input by a user, determining a second semantic keyword based on the second historical dialogue data, the current dialogue environment data, and the second input dialogue data, and generating and outputting, based on the second semantic keyword and a preset-type knowledge base, second feedback dialogue data corresponding to the second input dialogue data, where the preset-type knowledge base includes: a knowledge graph, text knowledge, and external knowledge other than the knowledge graph and the text knowledge.

Abstract: This application discloses a response determining method. The method includes: obtaining a to-be-responded first user statement; determining first state information of the first user statement based on the first user statement by using a state determining network, where the first state information includes a first dialog type of the first user statement; and inputting the first user statement and the first dialog type into a response generation network, to obtain a response corresponding to the first user statement.