HiRAG and RAGLAB
About HiRAG
hhy-huang/HiRAG
[EMNLP'25 findings] This is the official repo for the paper, HiRAG: Retrieval-Augmented Generation with Hierarchical Knowledge.
This project helps you get more accurate and comprehensive answers from large language models (LLMs) when querying your specific documents or knowledge base. You provide your textual content, and it processes it to enable a system that understands the hierarchical relationships within your information. The result is a more insightful and detailed response to your questions. This is for data scientists, researchers, or anyone building advanced question-answering systems over their proprietary data.
About RAGLAB
fate-ubw/RAGLAB
[EMNLP 2024: Demo Oral] RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
This project helps researchers and developers evaluate and compare different Retrieval-Augmented Generation (RAG) algorithms for large language models. It takes in various RAG algorithms and benchmark datasets, then outputs comprehensive evaluation results. It is ideal for AI researchers, NLP scientists, and machine learning engineers who need to understand, reproduce, and extend state-of-the-art RAG techniques.
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