公开(公告)号：US11216503B1

公开(公告)日：2022-01-04

申请号：US16696609

申请日：2019-11-26

Applicant: GOOGLE LLC

Inventor： Jilin Chen ,  Peng Dai ,  Lichan Hong ,  Tianjiao Zhang ,  Huazhong Ning ,  Ed Huai-Hsin Chi

IPC: G06F16/35

Abstract: Implementations provide an improved system for presenting search results based on entity associations of the search items. An example method includes generating first-level clusters of items responsive to a query, each cluster representing an entity in a knowledge base and including items mapped to the entity, merging the first-level clusters based on entity ontology relationships, applying hierarchical clustering to the merged clusters, producing final clusters, and initiating display of the items according to the final clusters. Another example method includes generating first-level clusters from items responsive to a query, each cluster representing an entity in a knowledge base and including items mapped to the entity, producing final clusters by merging the first-level clusters based on an entity ontology and an embedding space that is generated from an embedding model that uses the mapping, and initiating display of the items responsive to the query according to the final clusters.

公开(公告)号：US20250094838A1

公开(公告)日：2025-03-20

申请号：US18967327

申请日：2024-12-03

Applicant: Google LLC

Inventor： Jason Weng Wei ,  Dengyong Zhou ,  Xuezhi Wang ,  Dale Eric Schuurmans ,  Quoc V. Le ,  Maarten Paul Bosma ,  Ed Huai-Hsin Chi ,  Olivier Jean Andrè Bousquet ,  Le Hou ,  Charles Aloysius Sutton ,  Nathanael Martin Schärli ,  Nathan Kemp Sekiguchi Scales ,  Augustus Quadrozzi Odena ,  Sharan Ajit Narang ,  Guy Gur-Ari Krakover ,  Aakanksha Chowdhery ,  David Martin Dohan ,  Aitor Lewkowycz ,  Jacob Austin ,  Henryk Michalewski ,  David Luan ,  David J. Bieber ,  Anders Johan Andreassen ,  Maxwell Isaac Nye

IPC: G06N5/022

Abstract: An example technique for image analysis is provided. An example image analysis method includes obtaining an instructive sequence descriptive of an instructive query, an instructive response, and an instructive trace of intermediate states from the instructive query to the instructive response. The example image analysis method includes inputting, to a machine-learned model, the instructive sequence and an operative image processing query that comprises image data, wherein the machine-learned model is configured to process the operative query with attention over the instructive sequence. The example method can include generating, using the machine-learned model and responsive to the operative query, an operative image processing response that comprises an analysis of the image data.

公开(公告)号：US20240289395A1

公开(公告)日：2024-08-29

申请号：US18528142

申请日：2023-12-04

Applicant: Google LLC

Inventor： Hao Zhou ,  Shrestha Basu Mallick ,  Trevor Strohman ,  Patricia Luisa Romero Domingo ,  Amirhossein Kiani ,  Yu Du ,  Xinying Song ,  Heng-Tze Cheng ,  Quoc V. Le ,  Ed Huai-Hsin Chi ,  Christopher Jamie Maclean Hall

IPC: G06F16/9532 ,  G06F16/955 ,  G06F40/40

CPC classification number: G06F16/9532 ,  G06F16/955 ,  G06F40/40

Abstract: Implementations relate to helping a large language model generate factual responses to prompts that request factual content is disclosed. The large language model may receive a prompt context, a plurality of encoded context passages as input. The large language model is trained to determine whether or not to utilize the encoded context passages in generating the response. Implementations also relate to different methods of fine-tuning the responses generated by the large language model through query refinements, response re-writes, and evaluation of factual accuracy.

公开(公告)号：US20230394328A1

公开(公告)日：2023-12-07

申请号：US17881746

申请日：2022-08-05

Applicant: Google LLC

Inventor： Jason Weng Wei ,  Dengyong Zhou ,  Dale Eric Schuurmans ,  Quoc V. Le ,  Maarten Paul Bosma ,  Ed Huai-Hsin Chi ,  Olivier Jean Andrè Bousquet ,  Le Hou ,  Nathan Kemp Sekiguchi Scales ,  David J. Bieber ,  Charles Aloysius Sutton ,  Nathanael Martin Schärli ,  Augustus Quadrozzi Odena ,  Sharan Ajit Narang ,  Guy Gur-Ari Krakover ,  Aakanksha Chowdhery ,  Aitor Lewkowycz ,  Jiageng Luan ,  David Martin Dohan ,  Henryk Michalewski ,  Jacob Austin ,  Anders Johan Andreassen ,  Maxwell Isaac Nye ,  Xuezhi Wang

IPC: G06N5/02

CPC classification number: G06N5/022

Abstract: Example embodiments of aspects of the present disclosure provide an example computer-implemented method for improved prompting of a machine-learned model. The example method can include obtaining an instructive sequence descriptive of an instructive query, an instructive response, and an instructive trace of intermediate states from the instructive query to the instructive response. The example method can include inputting, to a machine-learned model, the instructive sequence and an operative query, wherein the machine-learned model is configured to process the operative query with attention over the instructive sequence. The example method can include generating, using the machine-learned model and responsive to the operative query, an operative response.

公开(公告)号：US20230244938A1

公开(公告)日：2023-08-03

申请号：US18160776

申请日：2023-01-27

Applicant: Google LLC

Inventor： Jason Weng Wei ,  Dengyong Zhou ,  Xuezhi Wang ,  Dale Eric Schuurmans ,  Quoc V. Le ,  Maarten Paul Bosma ,  Ed Huai-Hsin Chi ,  Olivier Jean Andrè Bousquet ,  Le Hou ,  Charles Aloysius Sutton ,  Nathanael Martin Schärli ,  Nathan Kemp Sekiguchi Scales ,  Augustus Quadrozzi Odena ,  Sharan Ajit Narang ,  Guy Gur-Ari Krakover ,  Aakanksha Chowdhery ,  David Martin Dohan ,  Aitor Lewkowycz ,  Henryk Michalewski ,  Jiageng Luan ,  David J. Bieber ,  Jacob Austin ,  Anders Johan Andreassen ,  Maxwell Isaac Nye ,  Yi Tay ,  Mostafa Dehghani

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: An example method for pretraining a machine-learned model is provided. The example method includes obtaining a plurality of different combinations of configuration parameters of a pretraining objective framework. The example method includes generating, using the pretraining objective framework, a plurality of corrupted training examples from one or more training examples, wherein the plurality of corrupted training examples are respectively generated according to the plurality of different combinations. The example method includes inputting the plurality of corrupted training examples into the machine-learned model, wherein the machine-learned model is configured to generate uncorrupted subportions corresponding to corrupted subportions of the corrupted training examples. The example method includes obtaining, from the machine-learned model, a plurality of outputs respectively generated by the machine-learned model based on the plurality of corrupted training examples. The example method includes updating one or more parameters of the machine-learned model based on an evaluation of the plurality of outputs.

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.