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docTTTTTquery

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About

docTTTTTquery is an advanced document expansion model that generates potential queries from input documents for improved retrieval relevance. This tool employs the T5 model architecture to predict questions that relate to the content, which are then appended to the original documents to enhance their indexability. By utilizing this approach, it aims to push complex neural computations to indexing time, making use of a 'bag of words' strategy for efficient querying against augmented document collections. The results indicate that docTTTTTquery performs better than its predecessor, doc2query, while maintaining a slightly increased latency compared to the BM25 model, exemplifying its effectiveness in improving query results. This model is particularly beneficial in information retrieval contexts, such as the MS MARCO passage ranking task, showcasing substantial improvements in the evaluation metrics of ranking data.

Platform
Web
Keywords
information retrievaldocument expansionquery predictiont5 modelms marco
Task
document expansion
Features

• competitive performance against other models

• improves indexing efficiency

• utilizes t5 model architecture

• enhances document retrieval relevance

• generates queries from input documents

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