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Retrieval-augmented question-answering systems combine retrieval techniques with large language models to provide answers that are more accurate and informative.
Bleu: a method for automatic evaluation of machine translation
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Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A. Smith. 2020 · 2020
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Realm: Retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang. 2020 · 2020
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Dense passage retrieval for open-domain question answering
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
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Unsupervised dense information retrieval with contrastive learning
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Large language model text generation inference
Huggingface. 2023 · 2023
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fastRAG: Efficient Retrieval Augmentation and Generation Framework
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
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Efficient memory management for large language model serving with pagedattention
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LangChain
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LlamaIndex
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In-context retrieval-augmented language models
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Replug: Retrieval-augmented black-box language models
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Stablelm 3b 4e1t
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SimLM: Pre-training with representation bottleneck for dense passage retrieval
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C-pack: Packaged resources to advance general chinese embedding
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