Generating a Diverse Set of LLM Responses
AssigneeGDM Holding LLC
InventorsFlorian Nils Hartmann, Matthew Sharifi
Life of this patent
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Abstract
Systems and methods for generating a diverse set of LLM responses can include processing input data to generate a first response, updating a corpus of output responses based on the first response, and processing the input data utilizing a sampling method that biases a sampling of an additional sequence of tokens associated with the additional sequence of tokens based on a distance metric. The second sampling method selects additional tokens that are diverse from tokens contained in the corpus of output responses based on the distance metric. The systems and methods can generate a diverse set of responses based on the additional tokens.
Claims20
A computer-implemented method comprising:
- processing input data using a sequence processing model to generate a first response as an output of the sequence processing model, wherein the first response comprises a first sequence of tokens;
- updating, based on the first response, a corpus of output responses;
- processing the input data using the sequence processing model to generate an additional response as an additional output of the sequence processing model, wherein:
- the additional response comprises an additional sequence of tokens;
- the sequence processing model utilizes a sampling method that biases a sampling of the additional sequence of tokens based on a distance metric, wherein the sampling method selects one or more additional tokens that are diverse from tokens contained in the corpus of output responses based on the distance metric;
- and
- the additional output is based on the one or more additional tokens that are diverse from the tokens contained in the corpus of output responses;
- and
- outputting the additional output to a user interface.
A computing system, comprising:
- one or more processors;
- and
- one or more non-transitory, computer-readable media storing instructions that, when implemented, cause the one or more processors to perform operations comprising:
- processing input data using a sequence processing model to generate a first response as an output of the sequence processing model, wherein the first response comprises a first sequence of tokens;
- updating, based on the first response, a corpus of output responses;
- processing the input data using the sequence processing model to generate an additional response as an additional output of the sequence processing model, wherein:
- the additional response comprises an additional sequence of tokens;
- the sequence processing model utilizes a sampling method that biases a sampling of candidate tokens associated with the additional sequence of tokens based on a distance metric, wherein the sampling method selects one or more additional tokens that are diverse from tokens contained in the corpus of output responses based on the distance metric;
- and
- the additional output is based on the one or more additional tokens that are diverse from the tokens contained in the corpus of output responses;
- and
- outputting the additional output to a user interface.
One or more non-transitory, computer-readable media storing instructions that, when implemented, cause one or more processors to perform operations, the operations comprising:
- processing input data using a sequence processing model to generate a first response as an output of the sequence processing model, wherein the first response comprises a first sequence of tokens;
- updating, based on the first response, a corpus of output responses;
- processing the input data using the sequence processing model to generate an additional response as an additional output of the sequence processing model, wherein:
- the additional response comprises an additional sequence of tokens;
- the sequence processing model utilizes a sampling method that biases a sampling of candidate tokens associated with the additional sequence of tokens based on a distance metric, wherein the sampling method selects one or more additional tokens that are diverse from tokens contained in the corpus of output responses based on the distance metric;
- and
- the additional output is based on the one or more additional tokens that are diverse from the tokens contained in the corpus of output responses;
- and
- outputting the additional output to a user interface.
Dependencies are inferred from the claim text.