Data efficient alignment of large language models
AssigneeNutanix, Inc.
InventorsRajat GHOSH, Debojyoti DUTTA, Amrit KHERA
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Abstract
Techniques for data efficient alignment of large language models retrieving a dataset comprising a plurality of alignment data elements; calculating an entropy for the dataset; for each alignment data element, calculate a respective entropy change value based on a difference between the entropy of the dataset and an entropy specific to the alignment data element; and generating an alignment training dataset comprising a subset of the plurality of alignment data elements that are identified based on the respective entropy change values.
Claims30
One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform steps of:
- retrieving a dataset comprising a plurality of alignment data elements;
- calculating an entropy for the dataset;
- for each alignment data element, calculate a respective entropy change value based on a difference between the entropy of the dataset and an entropy specific to the alignment data element;
- and
- generating an alignment training dataset comprising a subset of the plurality of alignment data elements that are identified based on the respective entropy change values.
A computer-implemented method for generating alignment training data, the method comprising:
- retrieving a dataset comprising a plurality of alignment data elements;
- calculating an entropy for the dataset;
- for each alignment data element, calculate a respective entropy change value based on a difference between the entropy of the dataset and an entropy specific to the alignment data element;
- and
- generating an alignment training dataset comprising a subset of the plurality of alignment data elements that are identified based on the respective entropy change values.
A system comprising:
- a memory storing instructions;
- and
- one or more processors coupled to the memory and, when executing the instructions, are configured to perform steps comprising:
- retrieving a dataset comprising a plurality of alignment data elements;
- calculating an entropy for the dataset;
- for each alignment data element, calculate a respective entropy change value based on a difference between the entropy of the dataset and an entropy specific to the alignment data element;
- and
- generating an alignment training dataset comprising a subset of the plurality of alignment data elements that are identified based on the respective entropy change values.
Dependencies are inferred from the claim text.