公开(公告)号：US20220108220A1

公开(公告)日：2022-04-07

申请号：US17493228

申请日：2021-10-04

Applicant: Google LLC

Inventor： Yao Qin ,  Alex Beutel ,  Ed Huai-Hsin Chi ,  Xuezhi Wang ,  Balaji Lakshminarayanan

IPC: G06N20/00

Abstract: Example aspects of the present disclosure are directed to systems and methods for performing automatic label smoothing of augmented training data. In particular, some example implementations of the present disclosure which in some instances can be referred to “AutoLabel” can automatically learn the labels for augmented data based on the distance between the clean distribution and augmented distribution. AutoLabel is built on label smoothing and is guided by the calibration-performance over a hold-out validation set. AutoLabel is a generic framework that can be easily applied to existing data augmentation methods, including AugMix, mixup, and adversarial training, among others. AutoLabel can further improve clean accuracy, as well as the accuracy and calibration over corrupted datasets. Additionally, AutoLabel can help adversarial training by bridging the gap between clean accuracy and adversarial robustness.

公开(公告)号：US20240330334A1

公开(公告)日：2024-10-03

申请号：US18225990

申请日：2023-07-25

Applicant: GOOGLE LLC

Inventor： Sidharth Mudgal ,  Ahmad Beirami ,  Jilin Chen ,  Alex Beutel ,  Harish Ganapathy ,  YaGuang Li ,  Tao Wang ,  Yanping Huang ,  Trevor Strohman

IPC: G06F16/332 ,  G06F40/284

CPC classification number: G06F16/3329 ,  G06F40/284

Abstract: Implementations relate to reducing latency in generating and/or rendering a given stream of natural language (NL) based output generated using a large language model (LLM). Processor(s) of a system can: receive NL based input associated with a client device, generate the stream of NL based output utilizing the LLM that is responsive to the NL based input and that is for a given dialog context of an ongoing dialog, and cause the stream of NL based output to be rendered at the client device. Notably, the processor(s) can employ attribute classifier(s) and a multi-objective scorer to implement a blockwise controlled decoding technique in generating the stream of NL based output utilizing the LLM. By implementing the blockwise controlled decoding technique in generating the stream of NL based output utilizing the LLM, the processor(s) can reduce latency in generating and/or of the stream of NL based output generated utilizing the LLM.