公开(公告)号：US20250045316A1

公开(公告)日：2025-02-06

申请号：US18788178

申请日：2024-07-30

Applicant: Google LLC

Inventor： Jinhyuk Lee ,  Zhuyun Dai ,  Xiaoqi Ren ,  Iftekhar Naim ,  Yi Luan ,  Blair Yuxin Chen ,  Siddhartha Reddy Jonnalagadda ,  Ming-Wei Chang ,  Daniel Matthew Cer ,  Gustavo Adolfo Hernandez Abrego ,  Jeremy Robert Cole ,  Colin Hearne Evans ,  Yuzhe Zhao ,  Pranay Bhatia ,  Rajvi Kapadia ,  Riham Hassan Abdel-Moneim Mansour ,  Raphael Dominik Hoffman ,  Simon Kunio Tokumine ,  Scott Bradley Huffman ,  Stephen Zachary Karukas ,  Michael Yiupun Kwong ,  Shu Zheng ,  Yan Qiao ,  Lukas Rutishauser ,  Anand Rajan Iyer

IPC: G06F16/33 ,  G06N3/08

Abstract: An example method includes providing, to a sequence model (i) a plurality of few-shot prompts, wherein each prompt comprises a demonstration passage, a demonstration task, and a demonstration query, wherein the demonstration task describes a type of retrieval, and wherein the demonstration query is relevant to the demonstration task, and (ii) a plurality of passages sampled from a corpus of passages. The method also includes receiving, from the sequence model and for the plurality of passages and based on the plurality of few-shot prompts, a respective plurality of predicted task-query pairs, the sequence model having been prompted to predict a task based on an input passage, and predict an output query relevant to the predicted task. The method further includes generating a synthetic training dataset comprising the plurality of passages and the respective plurality of predicted task-query pairs. The method also includes providing the synthetic training dataset.

公开(公告)号：US10685012B2

公开(公告)日：2020-06-16

申请号：US15424671

申请日：2017-02-03

Applicant: Google LLC

Inventor： Noam M. Shazeer ,  Colin Hearne Evans ,  Christopher Robert Waterson ,  Ryan P. Doherty

IPC: G06F16/23 ,  G06F16/31 ,  G06F16/332 ,  G06F16/33 ,  G06K9/62 ,  G06F40/44 ,  G06F17/16 ,  G06N5/04

Abstract: Methods, and systems, including computer programs encoded on computer storage media for generating compressed representations from a co-occurrence matrix. A method includes obtaining a set of sub matrices of a co-occurrence matrix, where each row of the co-occurrence matrix corresponds to a feature from a first feature vocabulary and each column of the co-occurrence matrix corresponds to a feature from a second feature vocabulary; selecting a sub matrix, wherein the sub matrix is associated with a particular row block and column block of the co-occurrence matrix; assigning respective d-dimensional initial row and column embedding vectors to each row and column from the particular row and column blocks, respectively; and determining a final row embedding vector and a final column embedding vector by iteratively adjusting the initial row embedding vectors and the initial column embedding vectors using the co-occurrence matrix.

公开(公告)号：US10460229B1

公开(公告)日：2019-10-29

申请号：US15464053

申请日：2017-03-20

Applicant: Google LLC

Inventor： Dayu Yuan ,  Ryan P. Doherty ,  Colin Hearne Evans ,  Julian David Christian Richardson ,  Eric E. Altendorf

IPC: G06F17/00 ,  G06N3/04 ,  G06F17/27

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for disambiguating word sense. One of the methods includes maintaining a respective word sense numeric representation of each of a plurality of word senses of a particular word; receiving a request to determine the word sense of the particular word when included in a particular text sequence, the particular text sequence comprising one or more context words and the particular word; determining a context numeric representation of the context words in the particular text sequence; and selecting a word sense of the plurality of word senses having a word sense numeric representation that is closest to the context numeric representation as the word sense of the particular word when included in the particular text sequence.