Invention Publication
- Patent Title: Instruction Fine-Tuning Machine-Learned Models Using Intermediate Reasoning Steps
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Application No.: US18424624Application Date: 2024-01-26
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Publication No.: US20240256965A1Publication Date: 2024-08-01
- Inventor: Hyung Won Chung , Barret Zoph , Dengyong Zhou , Liam Fedus , Shayne Longpre , Le Hou , Yi Tay , Jason Weng Wei , Siddhartha Brahma , Quoc V. Le
- Applicant: Google LLC
- Applicant Address: US CA Mountain View
- Assignee: Google LLC
- Current Assignee: Google LLC
- Current Assignee Address: US CA Mountain View
- Priority: SG 202300219X 2023.01.27
- Main IPC: G06N20/00
- IPC: G06N20/00

Abstract:
An example method for training a machine-learned sequence processing model includes obtaining a plurality of training examples for training the machine-learned sequence processing model. For each respective training example of the plurality of training examples, the example method includes: obtaining a respective query associated with the respective training example; inputting the respective query to the machine-learned sequence processing model; obtaining, from the machine-learned sequence processing model a response to the respective query and a trace of intermediate states from the respective query to the response; evaluating the response using a ground truth response associated with the respective training example; evaluating the trace using a ground truth trace associated with the respective training example; and updating one or more parameters of the machine-learned sequence processing model based on the evaluation of the response and based on the evaluation of the trace.
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