Highly Efficient Query Rewriter for Passage Retrieval in the realm of Retrieval-Augmented Generation (RAG)
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Updated
May 6, 2025 - Python
Highly Efficient Query Rewriter for Passage Retrieval in the realm of Retrieval-Augmented Generation (RAG)
AT-RAG (Adaptive Retrieval-Augmented Generation) is a novel RAG model developed to address the challenges of complex multi-hop queries
Implemented a question and answering model for multi-hop questions that requires logical inference or aggregation of information from various parts of the information text (like referring multiple wikis to answer a question)
Enhancing Retrieval-Augmented Generation with Document Link Structure for Multi-hop Web Question Answering
Modifications to Fusion in Decoder architecture to make more it efficient
Meandering In Networks of Entities to Reach Verisimilar Answers
MAT: Multihop Annotation Tool
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