US 2026/0087364 A1 · NOISE-BASED HALLUCINATION DETECTION IN GENERATIVE ARTIFICIAL INTELLIGENCE MODELS

Abstract

Techniques and apparatus for generating content using a generative artificial intelligence model are described. An example method generally includes receiving an input prompt for processing using a generative artificial intelligence model. An output of a layer of the generative artificial intelligence model is generated based on the input prompt and noise injected into the layer of the generative artificial intelligence model. A response to the input prompt is generated based on the output of the layer of the generative artificial intelligence model. Based on a probability distribution associated with the response to the input prompt, a likelihood that the response is a hallucinatory output of the generative artificial intelligence model is determined, and the generated response is output based on the determined likelihood that the response is a hallucinatory output.

Claims20

Dependent claims

A processing system for machine learning, comprising:

one or more memories comprising processor-executable instructions;
and
one or more processors coupled to the one or more memories and configured to execute the processor-executable instructions and cause the processing system to:
receive an input prompt for processing using a generative artificial intelligence model;
generate an output of a layer of the generative artificial intelligence model based on the input prompt and noise injected into the layer of the generative artificial intelligence model;
generate a response to the input prompt based on the output of the layer of the generative artificial intelligence model;
determine, based on a probability distribution associated with the response to the input prompt, a likelihood that the response is a hallucinatory output of the generative artificial intelligence model;
and
output the generated response based on the determined likelihood that the response is a hallucinatory output.

6 more elements

A processor-implemented method for machine learning, comprising:

receiving an input prompt for processing using a generative artificial intelligence model;
generating an output of a layer of the generative artificial intelligence model based on the input prompt and noise injected into the layer of the generative artificial intelligence model;
generating a response to the input prompt based on the output of the layer of the generative artificial intelligence model;
determining, based on a probability distribution associated with the response to the input prompt, a likelihood that the response is a hallucinatory output of the generative artificial intelligence model;
and
outputting the generated response based on the determined likelihood that the response is a hallucinatory output.

3 more elements

A processing system comprising:

means for receiving an input prompt for processing using a generative artificial intelligence model;
means for generating an output of a layer of the generative artificial intelligence model based on the input prompt and noise injected into the layer of the generative artificial intelligence model;
means for generating a response to the input prompt based on the output of the layer of the generative artificial intelligence model;
means for determining, based on a probability distribution associated with the response to the input prompt, a likelihood that the response is a hallucinatory output of the generative artificial intelligence model;
and
means for outputting the generated response based on the determined likelihood that the response is a hallucinatory output.

3 more elements

Dependencies are inferred from the claim text.

Noise-based hallucination detection in generative artificial intelligence models

Life of this patent

Noise-based hallucination detection in generative artificial intelligence models

Life of this patent

Abstract

Claims20