QingFei1/LongRAG
[EMNLP 2024] LongRAG: A Dual-perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering
This project helps you answer complex questions by drawing information from very long documents or multiple sources. It takes your extensive text data and a question as input, then generates accurate, detailed answers. Researchers, analysts, or anyone who needs to extract precise information from large knowledge bases will find this valuable.
120 stars. No commits in the last 6 months.
Use this if you need to accurately answer questions that require synthesizing information across vast amounts of text, where crucial details might be spread or deeply embedded.
Not ideal if your questions are simple or your documents are short, as the overhead of this system might be unnecessary.
Stars
120
Forks
14
Language
Python
License
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Category
Last pushed
Jan 29, 2025
Commits (30d)
0
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