Llm fine-tuning for text generation

A computing system, comprising:

• at least one processor connected to at least one memory;
• one or more non-transitory computer readable media including instructions stored thereon that when executed by at least the processor cause the computing system to:
• access a collection of text samples, wherein the text samples include a natural language text prompt that combines content and instructions;
• parse the text samples to extract the instructions from the text prompt;
• fine-tune a large language model to generate text in natural language based on a text generation loss function that penalizes non-compliance with one or more of the extracted instructions by a generated text response to the text prompt;
• generate an evaluation score for performance of the tuned large language model as a text generator based on a value of the text generation loss function for a second generated text response;
• and
• automatically determine to deploy the tuned large language model to a production environment for text generation in response to the evaluation score satisfying a threshold.

A computer-implemented method, comprising:

• accessing a collection of text samples, wherein the text samples include a natural language text prompt that combines content and instructions;
• extracting the instructions from the text prompt;
• fine-tuning a large language model to generate text in natural language based on a text generation loss function that penalizes non-compliance with one or more of the extracted instructions by a generated text response to the text prompt;
• generating an evaluation score for performance of the tuned large language model as a text generator based on a value of the text generation loss function for a second generated text response;
• and
• automatically signaling that the fine tuning of the tuned large language model is complete in response to the evaluation score satisfying a threshold.