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Retrieval-Augmented Generation (RAG) has gained significant attention in recent years for its potential to enhance natural language understanding and generation by combining large-scale retrieval systems with generative models.
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EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations
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Retrieval-Augmented Generation for Large Language Models: A Survey
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LightRAG: Simple and Fast Retrieval-Augmented Generation
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Late Chunking: Contextual Chunk Embeddings Using Long-Context Embedding Models
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How can we know when language models know? on the calibration of language models for question answering
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IDAS: Intelligent Driving Assistance System using RAG
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GRAG: Graph Retrieval-Augmented Generation
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REAPER: Reasoning based Retrieval Planning for Complex RAG Systems. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (Boise, ID, USA) (CIKM ’24) . Association for Computing Machinery, New York, NY, USA, 4621–4628
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HyPA-RAG: A Hybrid Parameter Adaptive Retrieval-Augmented Generation System for AI Legal and Policy Applications. In Proceedings of the 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U) . 237–256
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Empowering Automotive Software Development with LLM-RAG Integration-A study on leveraging the RAG-framework for AUTOSAR and automotive safety standards and specifications
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How to split text based on semantic similarity
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NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models
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