公开(公告)号：US20250078820A1

公开(公告)日：2025-03-06

申请号：US18741866

申请日：2024-06-13

Applicant: Google LLC

Inventor： Aishwariya Pattabiraman ,  Scott Bradley Huffman ,  Siddhartha Reddy Jonnalagadda ,  Ashwin Ram ,  Lee Boonstra ,  Erick Armbrust ,  Jack Fales ,  Yingchao Huang ,  Adrian Otto ,  Matthew O'Connor

IPC: G10L15/18 ,  G10L15/22

Abstract: Aspects of the disclosure are directed to a transactional agent for user interactions. The agent can seamlessly respond to user requests in a conversational manner while maintaining the conversational state. The agent can include a multi-stage modular model architecture, including a semantic understander and a semantic matcher. The semantic understander can be configured to understand common conversation conventions and/or patterns to produce a structure representation of a user request. The semantic matcher can be configured to map items and modifiers to product entries for a particular domain.

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.