US 2025/0097171 A1 · LLM FINE-TUNING FOR CHATBOT

Abstract

Systems, methods, and other embodiments automated fine-tuning of chatbot performance for large language models are described herein. In one embodiment, a method accesses a collection of sample conversations between two entities. An individual sample conversation includes one or more rounds of natural language example prompt by a querent and example response by an agent. The method fine-tunes an LLM to generate responses in natural language based on a chatbot loss function that evaluates first responses generated by the LLM to the example prompts by the querent. The method generates an evaluation score for performance of the tuned LLM as a chatbot based on second responses generated by the tuned LLM to test prompts from a test conversation. And, the method automatically signals that the fine-tuning of the tuned LLM is complete in response to the evaluation score satisfying a threshold.

Claims20

Dependent claims

One or more non-transitory computer-readable media that include stored thereon computer-executable instructions that when executed by at least a processor cause a computing system to:

access a collection of sample conversations between two entities, wherein an individual sample conversation includes one or more rounds of natural language example prompt by a customer and example response by an agent;
fine-tune a large language model to generate responses in natural language based on a chatbot loss function that evaluates first responses generated by the large language model to the example prompts by the customer;
generate an evaluation score for performance of the tuned large language model as a chatbot based on second responses generated by the tuned large language model to test prompts from a test conversation;
and
automatically determine to deploy the tuned large language model to a production environment to operate as a chatbot in response to the evaluation score satisfying a threshold.

2 more elements

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 sample conversations between two entities, wherein an individual sample conversation includes one or more rounds of natural language example prompt by a querent and example response by an agent;
fine-tune a large language model to generate responses in natural language based on a chatbot loss function that evaluates first responses generated by the large language model to the example prompts by the querent;
generate an evaluation score for performance of the tuned large language model as a chatbot based on second responses generated by the tuned large language model to test prompts from a test conversation;
and
automatically deploying the tuned large language model to a production environment to operate as a chatbot in response to the evaluation score satisfying a threshold.

4 more elements

A computer-implemented method, comprising:

accessing a collection of sample conversations between two entities, wherein an individual sample conversation includes one or more rounds of natural language example prompt by a querent and example response by an agent;
fine-tuning a large language model to generate responses in natural language based on a chatbot loss function that evaluates first responses generated by the large language model to the example prompts by the querent;
generating an evaluation score for performance of the tuned large language model as a chatbot based on second responses generated by the tuned large language model to test prompts from a test conversation;
and
automatically signaling that the fine-tuning of the tuned large language model to operate as a chatbot is complete in response to the evaluation score satisfying a threshold.

2 more elements

Dependencies are inferred from the claim text.

Llm fine-tuning for chatbot

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Llm fine-tuning for chatbot

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Abstract

Claims20