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Retrieval-Augmented Generation (RAG) has gained significant popularity in modern Large Language Models (LLMs) due to its effectiveness in introducing new knowledge and reducing hallucinations.
Some complexity questions related to distributive computing(preliminary report)
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Atlas: Few-shot learning with retrieval augmented language models
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White-box transformers via sparse rate reduction
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The expressive power of low-rank adaptation
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The power of noise: Redefining retrieval for rag systems
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Bias and unfairness in information retrieval systems: New challenges in the llm era
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Towards revealing the mystery behind chain of thought: a theoretical perspective
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Compression represents intelligence linearly
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