System and method of programmer-interpreter approach for large language model post-editing

A text generation system comprising:

• an input interface configured to receive input;
• a large language model based generator configured to translate the input into an initial output text using at least a set of examples retrieved by a function in the generator when performing in-context learning;
• a programmer configured to encode domain task-specific knowledge;
• an interpreter configured to facilitate domain generalization using cross-domain knowledge,
• wherein the programmer and the interpreter are collectively configured to refine the initial output text or intermediate output text by encoding domain task-specific knowledge and facilitating domain generalization, respectively, and
• wherein the programmer and the interpreter are collectively configured to improve a quality of the initial output text or the intermediate output text in low-resource cross-domain text generation tasks by exploiting the encoding of domain task-specific knowledge by the interpreter and facilitation of domain generalization by the interpreter;
• and
• an output interface configured to output text based on the initial output text and the intermediate output text.

A computer-implemented system for text generation enhancement, comprising:

• a processor configured to receive input text from a user;
• a generator, a programmer, and an interpreter communicatively coupled to the processor, wherein the programmer is configured to encode domain task-specific knowledge and the interpreter is configured to facilitate domain generalization using cross-domain knowledge;
• and
• the processor further configured to iteratively refine an initial output text generated by the generator and intermediate output text generated by the interpreter using the programmer and the interpreter to enhance large language model performance in low-resource cross-domain text generation tasks.