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：US20240249080A1

公开(公告)日：2024-07-25

申请号：US18128450

申请日：2023-03-30

Applicant: Google LLC

Inventor： Ruoxi Sun ,  Xingchen Wan ,  Hanjun Dai ,  Sercan Omer Arik ,  Tomas Pfister

IPC: G06F40/40 ,  G06F16/33

CPC classification number: G06F40/40 ,  G06F16/3344

Abstract: Aspects of the disclosure are directed to automatically selecting examples in a prompt for an LLM to demonstrate how to perform tasks. Aspects of the disclosure can select and build a set of examples from LLM zero-shot outputs via predetermined criteria that can combine consistency, diversity, and repetition. In the zero-shot setting for three different LLMs, using only LLM predictions, aspects of the disclosure can improve performance up to 15% compared to zero-shot baselines and can match or exceed few-shot base-lines for a range of reasoning tasks.