Method for generating data based on large model, electronic device and storage medium

An electronic device, comprising:

• at least one processor;
• and
• a memory connected in communication with the at least one processor;
• wherein the memory stores an instruction executable by the at least one processor, and the instruction, when executed by the at least one processor, enables the at least one processor to execute:
• inputting target prompt information into a generative large model, such that the generative large model generates first question information based on expert knowledge and a target action sequence in the target prompt information, wherein the target action sequence is configured to constrain an execution path of the generative large model;
• the first question information includes text data, and the text data is a clue required for answering the first question information;
• verifying the first question information based on an evaluation large model to obtain a difficulty level, wherein the difficulty level is used to represent a degree of difficulty for the target large model to answer the first question information;
• and
• constructing a training sample for the target large model based on the first question information in the case where the difficulty level is a target level.

A non-transitory computer-readable storage medium storing a computer instruction thereon, wherein the computer instruction is used to cause a computer to execute:

• inputting target prompt information into a generative large model, such that the generative large model generates first question information based on expert knowledge and a target action sequence in the target prompt information, wherein the target action sequence is configured to constrain an execution path of the generative large model;
• the first question information includes text data, and the text data is a clue required for answering the first question information;
• verifying the first question information based on an evaluation large model to obtain a difficulty level, wherein the difficulty level is used to represent a degree of difficulty for the target large model to answer the first question information;
• and
• constructing a training sample for the target large model based on the first question information in the case where the difficulty level is a target level.