公开(公告)号：US20240378196A1

公开(公告)日：2024-11-14

申请号：US18684518

申请日：2021-08-20

Applicant: Google LLC

Inventor： Brian David Lester ,  Rami Eid Sammour Al-Rfou ,  Noah JG Constant

IPC: G06F16/242

Abstract: Systems and methods for prompt tuning can leverage semantic searching for determining similar prompts to use for retraining. A prompt can be generated then searched to find the similar prompts. Data related to the similar prompts can then be utilized for prompt tuning. Moreover, systems and methods for prompt tuning can generate and utilize a meta-prompt to reduce the computational cost of generating prompts. The prompt tuning techniques can be implemented as part of a prompt tuning application programming interface (API).

公开(公告)号：US20230325725A1

公开(公告)日：2023-10-12

申请号：US17718738

申请日：2022-04-12

Applicant: Google LLC

Inventor： Brian David Lester ,  Rami Al-Rfou ,  Noah Constant

IPC: G06N20/20 ,  G06V10/764 ,  G06V10/774

CPC classification number: G06N20/20 ,  G06V10/764 ,  G06V10/7747

Abstract: Systems and methods for natural language processing can leverage trained prompts to condition a large pre-trained machine-learned model to generate an output for a specific task. For example, a subset of parameters may be trained for the particular task to then be input with a set of input data into the pre-trained machine-learned model to generate the task-specific output. During the training of the prompt, the parameters of the pre-trained machine-learned model can be frozen, which can reduce the computational resources used during training while still leveraging the previously learned data from the pre-trained machine-learned model.

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.