Google Inc.

Michael Bendersky

Marc Alexander Najork

Mingyang Zhang

Cheng Li

Timothy Youngjin Sohn

Sujith Ravi

Xuanhui Wang

Liu Yang

Thomas Nelson

Brandon Vargo

Peter Shao-sen Young

Remy Burger

Tao Chen

Michael Colagrosso

Spurthi Amba Hombaiah

Mike Colagrosso

Shuguang Han

Sergey Novikov

Przemysław Gajda

Zachary Dicklin

Weize Kong

Te-Lin Wu

A method for providing personalized responses to textual prompts using a large scale, privacy preserving, large language model (LLM) includes receiving a textual prompt from a user specifying a task for an LLM to perform, and obtaining a set of user features associated with the user. The method also includes determining, using the set of user features associated with the user, a user prompt embedding for the user, and processing, using the LLM, the textual prompt conditioned on the user prompt embedding for the user to generate a personalized response to the textual prompt. The method further includes providing the personalized response to the textual prompt for output from a user device associated with the user.

Large-scale, privacy preserving personalized large language models (LLMs)

Systems and methods for document processing that can process and understand the layout, text size, text style, and multimedia of a document can generate more accurate and informed document representations. The layout of a document paired with text size and style can indicate what portions of a document are possibly more important, and the understanding of that importance can help with understanding of the document. Systems and methods utilizing a hierarchical framework that processes the block-level and the document-level of a document can capitalize on these indicators to generate a better document representation.

Layout-aware multimodal pretraining for multimodal document understanding

Systems and methods include pre-processing documents in cloud storage using query embeddings, providing personalized prompts to users based on documents in cloud storage, real-time anticipation of user interest in information contained in documents in cloud storage, and providing generative answers including citation to source documents in cloud storage. The system and methods generate generative machine learning model (MLM) prompts based on document portions of documents in a cloud-based content management platform. The systems and methods use the generative MLM to generate responses to prompts, and the responses include citations to the document portions used to generate the responses in order for users to verify the responses.

Providing generative answers including citations to source documents

Systems and methods of the present disclosure are directed to a method for predicting semantic similarity between documents. The method can include obtaining a first document and a second document. The method can include parsing the first document into a plurality of first textual blocks and the second document into a plurality of second textual blocks. The method can include processing each of the plurality of first textual blocks and the second textual blocks with a machine-learned semantic document encoding model to obtain a first document encoding and a second document encoding. The method can include determining a similarity metric descriptive of a semantic similarity between the first document and the second document based on the first document encoding and the second document encoding.

Systems and methods for machine-learned prediction of semantic similarity between documents

Techniques of generating recrawl policies for commercial offer pages include generating a multiple strategy approach using a number of different strategies. In some implementations, each strategy is an arm of a K-armed adversarial bandits algorithm with reinforcement learning. Moreover, in some implementations, the multiple strategy approach also uses a machine learning algorithm to estimate parameters such as a click rate, impression rate, and likelihood of price change, i.e., change rate, which was assumed known in the conventional approaches.

Adversarial bandits policy for crawling highly dynamic content

Techniques are described herein for enabling more computationally efficient organization of files within a cloud storage system. A method includes: receiving information identifying a document and a set of folders; for each folder in the set of folders, using a trained model to predict a similarity measure between the folder and the document; for each folder in the set of folders, determining a score for the folder based on the predicted similarity measure for the folder; selecting a candidate folder from the set of folders using the scores of the folders within the set of folders; and providing, on a user interface, a selectable option to associate the document with the candidate folder.

Automatic file organization within a cloud storage system

Methods, systems, apparatus, including computer programs encoded on computer storage medium, to facilitate identification of additional trigger-terms for a structured information card. In one aspect, the method includes actions of accessing data associated with a template for presenting structured information, wherein the accessed data references (i) a label term and (ii) a value. Other actions may include obtaining a candidate label term, identifying one or more entities that are associated with the label term, identifying one or more of the entities that are associated with the candidate label term, and for each particular entity of the one or more entities that are associated with the candidate label term, associating, with the candidate label term, (i) a label term that is associated with the particular entity, and (ii) the value associated with the label term.

Search and retrieval of structured information cards

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Sunnyvale, CA, United States of America

Michael Bendersky