s-nlp/AdaRAGUE
[ACL 2025] Adaptive Retrieval without Self-Knowledge? Bringing Uncertainty Back Home
This is a collection of code and datasets for researchers working on advanced search and question-answering systems. It helps evaluate and implement different 'Adaptive Retrieval-Augmented Generation' (RAG) methods, which are techniques for improving how AI models answer questions by fetching relevant information. Researchers can input various natural language datasets and test how different retrieval methods impact the quality of the answers.
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Use this if you are a natural language processing researcher or practitioner experimenting with and comparing the performance of different adaptive retrieval methods for question answering.
Not ideal if you are looking for a ready-to-use application or a simple API to integrate adaptive RAG into an existing product without needing to understand the underlying research implementations.
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Python
License
MIT
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Last pushed
May 17, 2025
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