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：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.