XieZilongAI/E2E-AFG
An End-to-End Model with Adaptive Filtering for Retrieval-Augmented Generation
This project helps research scientists and knowledge managers improve the accuracy of question-answering systems. It takes a question and a set of related documents (like Wikipedia articles) and produces a precise answer, sifting through irrelevant information. This is for someone who needs to generate highly accurate answers from large document sets, like a researcher reviewing scientific literature or a professional summarizing market intelligence.
No commits in the last 6 months.
Use this if you need to build a question-answering system that can intelligently filter out noisy or irrelevant information from source documents to provide more accurate responses.
Not ideal if you are looking for a simple plug-and-play solution without needing to train or fine-tune models on your specific datasets.
Stars
16
Forks
2
Language
Python
License
—
Category
Last pushed
Oct 27, 2024
Commits (30d)
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