公开(公告)号：US20240020546A1

公开(公告)日：2024-01-18

申请号：US17863840

申请日：2022-07-13

Applicant: Google LLC

Inventor： Tu Thanh Vu ,  Daniel Matthew Cer ,  Noah Constant ,  Brian David Lester ,  Rami Al-Rfou

IPC: G06N5/02

CPC classification number: G06N5/022

Abstract: Systems and methods for prompt tuning can utilize previously-learned prompts for the initialization of tuning for prompts on different tasks that may differ from the task associated with the previously-learned prompt. The prompt being utilized for initialization can be a generic prompt and/or may be a prompt selected based on a determined similarity between two or more task embeddings.

公开(公告)号：US20220383206A1

公开(公告)日：2022-12-01

申请号：US17826690

申请日：2022-05-27

Applicant: Google LLC

Inventor： Thang Minh Luong ,  Tu Thanh Vu ,  Quoc V. Le ,  Grady Hayes Simon

IPC: G06N20/20 ,  G06K9/62

Abstract: Systems and methods can leverage task-specific unlabeled data to improve downstream performance in data-constrained scenarios. Given a target task, a first technique proposed herein, which can be referred to as task augmentation, uses unlabeled text from the target domain to synthesize a large amount of in-domain training data for an auxiliary task A second technique provides a self-training algorithm, where a model learns to improve itself using its predictions on unlabeled examples.