US 2026/0087367 A1Published application

Automatic confidence based detection for fine-tuning of large language models

AssigneeIntuit Inc.

InventorsKun HU, Jiaxin ZHANG

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Abstract

Systems and methods are disclosed for fine-tuning a large language model (LLM). In particular, a system calculates when to again fine-tune an LLM based on a newly obtained dataset. The system identifies whether the LLM's use of the new dataset causes more than a tolerable difference in the outputs to a defined set of inputs. As such, the system uses the LLM to generate a new set of outputs and corresponding set of confidences for the defined set of inputs based on the new dataset, and the system generates a new distribution of the new set of confidences. The system also stores a previous distribution of a previous set of confidences in a previous set of outputs to the defined set of inputs based on a previous dataset, and the system calculates a difference between the new distribution and the previous distribution. Fine-tuning the LLM is based on the difference.

Claims20

Dependent claims

A computer-implemented method for fine-tuning a large language model (LLM), the method comprising:

providing a set of inputs to a fine-tuned LLM;
generating, by the fine-tuned LLM, a first set of outputs based on the fine-tuned LLM analyzing a first dataset to respond to the set of inputs;
generating, by the fine-tuned LLM, a first set of confidences for the first set of outputs, wherein each confidence of the first set of confidences corresponds to an output of the first set of outputs;
obtaining a second dataset;
generating, by the fine-tuned LLM, a second set of outputs based on the fine-tuned LLM analyzing the second dataset to respond to the set of inputs;
generating, by the fine-tuned LLM, a second set of confidences for the second set of outputs, wherein each confidence of the second set of confidences corresponds to an output of the second set of outputs;
calculating a difference between a first distribution of the first set of confidences and a second distribution of the second set of confidences;
determining whether to fine-tune the fine-tuned LLM using the second dataset, wherein the determination is based on the difference;
and
fine-tuning the fine-tuned LLM using the second dataset based on the determination.

7 more elements

A computing system for fine-tuning a large language model (LLM), the system comprising:

one or more processors;
and
a memory storing instructions that, when executed by the one or more processors, causes the system to perform operations comprising:
providing a set of inputs to a fine-tuned LLM;
generating, by the fine-tuned LLM, a first set of outputs based on the fine-tuned LLM analyzing a first dataset to respond to the set of inputs;
generating, by the fine-tuned LLM, a first set of confidences for the first set of outputs, wherein each confidence of the first set of confidences corresponds to an output of the first set of outputs;
obtaining a second dataset;
generating, by the fine-tuned LLM, a second set of outputs based on the fine-tuned LLM analyzing the second dataset to respond to the set of inputs;
generating, by the fine-tuned LLM, a second set of confidences for the second set of outputs, wherein each confidence of the second set of confidences corresponds to an output of the second set of outputs;
calculating a difference between a first distribution of the first set of confidences and a second distribution of the second set of confidences;
determining whether to fine-tune the fine-tuned LLM using the second dataset, wherein the determination is based on the difference;
and
fine-tuning the fine-tuned LLM using the second dataset based on the determination.

10 more elements

Dependencies are inferred from the claim text.