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Retrieval-Augmented Generation (RAG) models are designed to incorporate external knowledge, reducing hallucinations caused by insufficient parametric (internal) knowledge.
Gershgorin’s theorem and the zeros of polynomials
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Interpreting GPT: the logit lens
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A mathematical framework for transformer circuits
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Transformer feed-forward layers are key-value memories
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Incorporating residual and normalization layers into analysis of masked language models
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Retrieval augmentation reduces hallucination in conversation
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How to remove or control confounds in predictive models, with applications to brain biomarkers
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Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 2022
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Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al · 2022
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Out-of-distribution detection and selective generation for conditional language models
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Confident adaptive language modeling
Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Q. Tran, Yi Tay, and Donald Metzler · 2022
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Near-lossless acceleration of long context llm inference with adaptive structured sparse attention
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RAGAs: Automated evaluation of retrieval augmented generation
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A survey on rag meeting llms: Towards retrieval-augmented large language models
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A primer on the inner workings of transformer-based language models
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Retrieval-augmented generation for large language models: A survey
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