Google Inc.

Vikram Sridar

Evgeny A Cherepanov

Ishai Rabinovitz

Omer Tabach

Jakob Nicolaus Foerster

Quoc V Le

Noam M Shazeer

Martin Baeuml

Thushan Amarasiriwardena

Daniel De Freitas Adiwardana

Roberto Pieraccini

As part of a dialog session between a user and an automated assistant, implementations can receive a stream of audio data that captures a spoken utterance including an assistant query, determine, based on processing the stream of audio data, a set of assistant outputs that are each predicted to be responsive to the assistant query, process, using large language model (LLM) output(s), the assistant outputs and context of the dialog session to generate a set of modified assistant outputs, and cause given modified assistant output, from among the set of modified assistant outputs, to be provided for presentation to the user in response to the spoken utterance. In some implementations, the LLM output(s) can be generated in an offline manner for subsequent use in an online manner. In additional or alternative implementations, the LLM output(s) can be generated in an online manner when the spoken utterance is received.

Using large language model(s) in generating automated assistant response(s

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for natural language processing. One of the method includes receiving a voice input from a user device; generating a recognition output; receiving a user selection of one or more terms in the recognition output; receiving a user input of one or more letters replacing the user selected one or more terms; determining suggested correction candidates based in part on the user input and the voice input; and providing one or more suggested correction candidates to the user device as suggested corrected recognition outputs.

Biasing voice correction suggestions

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Zurich, Switzerland

Vikram Sridar