公开(公告)号：US20250045575A1

公开(公告)日：2025-02-06

申请号：US18423802

申请日：2024-01-26

Applicant: Roku, Inc.

Inventor： Abhishek Majumdar ,  Kapil Kumar ,  Nitish Aggarwal ,  Danish Nasir Shaikh ,  Manasi Deshmukh ,  Apoorva Jakalannanavar Halappa Manjula

IPC: G06N3/08

Abstract: Pre-trained large language models may be trained on a large data set which may not necessarily align with specific tasks, business goals, and requirements. Pre-trained large language models can solve generic semantic relationship or question-answering type problems but may not be suited for content item retrieval or recommendation of content items that are semantically relevant to a query. It is possible to build a machine learning model while using transfer learning to learn from pre-trained large language models. Training data can significantly impact the performance of machine learning models, especially machine learning models developed using transfer learning. The training data can impact a model's performance, generalization, fairness, and adaptation to specific domains. To address some of these concerns, a popularity bucketing strategy can be implemented to debias training data. Optionally, an ensemble of models can be used to generate diverse training data.