Context-driven fine-tuning for reliable retrieval augmented generation

A method comprising:

• identifying a question for a large language model (LLM);
• identifying a context data item for the first question;
• identifying a response that is based on the question and the context data item;
• inputting, into by the LLM, the question and the context data item, resulting in the LLM generating a first response;
• generating, by the LLM, based on the question and without the context data item, a second response;
• generating a training instance that comprises the question, the context data item, the first response as a correct answer, and the second response as a rejected answer;
• fine tuning a machine-learned model based on the training instance;
• wherein the method is performed by one or more computing devices.

One or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause:

• identifying a question for a large language model (LLM);
• identifying a context data item that is in an incorrect context relative to the question;
• inputting, into by the LLM, the question and the context data item, resulting in the LLM generating a response;
• generating a training instance that comprises the question, the context data item, a deny response as a correct answer, and the response as a rejected answer;
• fine tuning a machine-learned model based on the training instance;
• wherein the method is performed by one or more computing devices.