公开(公告)号：US20240394545A1

公开(公告)日：2024-11-28

申请号：US18377368

申请日：2023-10-06

Applicant: Google LLC

Inventor： Julian Martin Eisenschlos ,  Xingchen Wan ,  Hootan Nakhost ,  Sercan Omer Arik ,  Ruoxi Sun ,  Hanjun Dai

IPC: G06N3/088 ,  G06N3/0455

Abstract: Aspects of the disclosure are directed to methods, systems, and computer readable media for universal self-adaptive prompting (USP), which includes an automatic prompt design approach specifically tailored for zero-shot learning, though still compatible with few-shot learning. To achieve universal prompting, USP categorizes a natural language processing (NLP) task into one of a plurality of possible task types and then uses a corresponding selector to select the most suitable queries and zero-shot model-generated responses as pseudo-demonstrations, thereby generalizing in-context learning to the zero-shot setup in a fully automated manner.

公开(公告)号：US20240362212A1

公开(公告)日：2024-10-31

申请号：US18225277

申请日：2023-07-24

Applicant: Google LLC

Inventor： Ruoxi Sun ,  Sercan Omer Arik ,  Rajarishi Sinha ,  Hootan Nakhost ,  Hanjun Dai ,  Pengcheng Yin

IPC: G06F16/2452 ,  G06F16/242

CPC classification number: G06F16/24522 ,  G06F16/2433

Abstract: Aspects of the disclosure are directed to methods, systems, and non-transitory computer readable media for automatically generating queries on a database from natural language text using in-context learning to leverage zero-shot and few-shot adaptation capabilities of large language models (LLMs). The methods, systems, and non-transitory computer readable media can consider database information, employ execution based consistency decoding, and employ a mixture of prompts and/or LLMs.