DaoD/SPRING
[AAAI'25] SPRING: Learning Scalable and Pluggable Virtual Tokens for Retrieval-Augmented Large Language Models
This project helps developers enhance the factual accuracy and relevance of large language models (LLMs) for tasks like question-answering, without compromising the LLMs' original capabilities. It takes a pre-trained LLM and specialized 'virtual token embeddings' as input, then outputs an LLM that is better at incorporating retrieved information. This is for AI/ML engineers and researchers who are building applications that use Retrieval-Augmented Generation (RAG).
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Use this if you need to significantly improve the performance of your LLMs in RAG scenarios, particularly for question-answering, while preserving their general generation abilities.
Not ideal if you are looking for a complete RAG system; this project focuses specifically on the LLM fine-tuning aspect using virtual tokens.
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Language
Python
License
MIT
Category
Last pushed
Sep 24, 2025
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