Adobe, Inc.

Kenneth George Russell

Manas Garg

Eunyee Koh

Shivakumar Vaithyanathan

Anup Bandigadi Rao

Saayan Mitra

Tong Yu

Xiang Chen

Uttaran Bhattacharya

Victor Soares Bursztyn

Aleksander Pejcic

Akash Vivek Maharaj

Vaishnavi Muppala

Anshul Verma

Alexandru Ionut Hodorogea

Saurabh Tripathy

Ishita Dasgupta

Some aspects relate to technologies for an artificial intelligent (AI) system that, among other things, enhances responses to concepts questions for an application with contextual usage insights. In accordance with some aspects, a user query is determined to comprise a concepts question regarding an application. Responsive to determining the user query comprises the concepts question, documentation regarding the application relevant to the user query is identified. A generative model generates text for a response to the concepts question using the documentation regarding the application. Additionally, a determination is made to add contextual usage insights to the response. Responsive to determining to add contextual usage insights to the response, usage data relevant to the user query and/or the response is retrieved. The generative model generates text for a final response using the response and the usage data, and the final response is provided to a user device for presentation.

Enhancing artificial intelligence responses with contextual usage insights

A method, apparatus, non-transitory computer readable medium, and system for document retrieval include obtaining a query and a document. A prompt generator generates a prompt for a reasoning model based on the query and the document. The reasoning model generates a reasoning result based on the prompt. In some cases, the reasoning result indicates that the document answers the query. A machine learning model provides the document in response to the query based on the reasoning result.

In-context and semantic-aware ensemble model for document retrieval

Techniques and systems are described to control a determination of document similarity. In one example, dimensionality of the documents is reduced through computation of a signature, e.g., via a hashing technique such as 'minhashing' which is also known as min-wise independent permutations locality sensitive hashing. From these signatures, another hashing technique (e.g., locality sensitive hashing) is used to determine similarity of the signatures to each other. Identification of disjoint sets is then used as a basis to partition the documents for determination of document similarity by respective nodes of a plurality of computing devices. In this way, an amount of data shuffling between the nodes as part of the determination of document similarity may be reduced. In another example, a weighting is applied to attributes of documents as part of the determination of document similarity.

Control of document similarity determinations by respective nodes of a plurality of computing devices

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Mountain View, CA, United States of America

Kenneth George Russell