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：US20250094456A1

公开(公告)日：2025-03-20

申请号：US18887751

申请日：2024-09-17

Applicant: GOOGLE LLC

Inventor： Kelvin Gu ,  Zhuyun Dai ,  Panupong Pasupat ,  Chen Elkind ,  Eran Ofek ,  Hagai Taitelbaum ,  Mukund Sundararajan ,  Vered Cohen ,  Itay Karo ,  Norbert Kalb ,  Yossi Matias ,  Tej Toor ,  Teghan Tracy

IPC: G06F16/332 ,  G06F16/33 ,  G06F16/35

Abstract: Implementations are described herein for identifying potentially false information in generative model output by performing entailment evaluation of generative model output. In various implementations, data indicative of a query may be processed to generate generative model output. Textual fragments may be extracted from the generative model output, and a subset of the textual fragments may be classified as being suitable for textual entailment analysis. Textual entailment analysis may be performed on each textual fragment of the subset, including formulating a search query based on the textual fragment, retrieving document(s) responsive to the search query, and processing the textual fragment and the document(s) using entailment machine learning model(s) to generate prediction(s) of whether the at least one document corroborates or contradicts the textual fragment. When natural language (NL) responsive to the query is rendered at a client device, annotation(s) may be rendered to express the prediction(s).