Back to results
US 2026/0017522 A1Published application
Enterprise-specific language model training techniques
AssigneeOptum, Inc.
InventorsJoel David STREMMEL, Sanjit Singh BATRA, Jun HAN
Life of this patent
Estimating the term…
Loading patent…
Loading patent…
AssigneeOptum, Inc.
InventorsJoel David STREMMEL, Sanjit Singh BATRA, Jun HAN
Estimating the term…
Various embodiments of the present disclosure provide a language model training technique. The language model training technique may include a data blending preprocessing step to improve the performance of the language model at an enterprise level. The data blending technique includes receiving an enterprise data partition from a plurality of enterprise data partitions associated with an enterprise data source, receiving a domain-specific data partition from a plurality of domain-specific data partitions associated with one or more domain data sources that are different than the enterprise data source, storing the enterprise data partition as an initial training partition of a plurality of balanced training partitions within a balanced training dataset, and generating a balanced training partition by appending a portion of the domain-specific data partition to the initial training partition. A domain-specific language model may then be trained based on the balanced training dataset.
A computer-implemented method comprising:
A system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:
One or more non-transitory computer-readable storage media including instructions that, when executed by one or more processors, cause the one or more processors to:
Dependencies are inferred from the claim text.