US 2024/0202452 A1 · PROMPT GENERATION SIMULATING FINE-TUNING FOR A MACHINE LEARNING MODEL

Abstract

Aspects of the present disclosure relate to systems and methods for generating one or more prompts based on an input and the semantic context associated with the input. In examples, the prompts may be provided as input to one or more general ML models to provide a semantic context around the input and/or output of the model. The prompt simulates training and fine-tuned specialization of the general ML model without the need to use a fine-tuning process to actually train the general ML model into a fine-tuned state. Additionally, the model output may be evaluated for responsiveness to the input prior to being returned to the user. An advantage of the present disclosure is that it allows a general ML model to be applied to a plurality of applications without the need for expensive and time-consuming training to fine-tune the ML model.

Claims20

Dependent claims

A system comprising:

at least one processor;
and
memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, the set of operations comprising:
receive an input;
generate a task request based on the received input;
determine a task objective based on the input and task request;
generate one or more prompts based upon the task objective and task request, wherein the one or more prompts include additional context relating to the input for a machine learning model;
process the one or more prompts with the machine learning model to generate a model output;
and
provide the model output to an application of a computing device.

7 more elements

Dependencies are inferred from the claim text.

Prompt generation simulating fine-tuning for a machine learning model

Life of this patent

Prompt generation simulating fine-tuning for a machine learning model

Life of this patent

Abstract

Claims20