Salesforce, Inc.

Ye Liu

Yingbo Zhou

Semih Yavuz

Rui Meng

Caiming Xiong

Tong Niu

Ning Yu

Shafiq Rayhan Joty

Embodiments described herein provide a method for building an artificial intelligence (AI) agent to respond to a user query. The method includes: receiving a user query; retrieving a set of documents that are ranked based on respective relevance scores of a first type to the user query; generating a core question that filters out irrelevant texts from the user query; generating a first summary of a first document from the set of documents and a first reasoning output explaining how the first summary addresses the core question; generating a relevance score of a second type and a corresponding reranking for the first document based at least in part on a combination of the core question and the first reasoning output; generating a response to the user query using one or more top-ranked documents according to generated rerankings of the set of documents.

Systems and methods for a reasoning-intensive reranking based artificial intelligence conversation agent

Embodiments described herein provide systems and methods for training a text retrieval model. A system may generate queries associated with provided documents. The queries may be generated in one or more different manners. Examples of query generation may include extracting relevant spans of text from the documents, prompting a language model for a topic, title, abstractive summary, and/or extractive summary based on the documents. Metadata such as title or other HTML tags may be used as queries. Using the one or more queries, the text retrieval model may be trained using contrastive learning, using the generated query, and positive and negative sample documents. A fine-tuning training phase may be performed using domain-specific data which may also be done with generated query pairs, or may be done in a supervised fashion with provided queries. The text retrieval model may be used to locate documents given an input query.

Systems and methods for unsupervised training in text retrieval tasks

Embodiments described herein provide a semantic parsing framework which may be referred to as Uni-Parser. The Uni-Parser framework may be applied to question answering on both knowledge bases and databases. The three main stages of the Uni-Parser framework are enumeration, ranking, and generation. At the enumeration stage, primitives are enumerated based on matching the question to the data structure. After enumerating primitives, the Uni-Parser framework may rank the primitives used a trained ranker model. The top ranked primitives may then be used as inputs to a generator which is a learned sequence to sequence model which produces a logical form.

Systems and methods for semantic parsing with primitive level enumeration

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Fremont, CA, United States of America

Ye Liu