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2024

C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System

Addison, Parker, Nguyen, Minh-Tuan H., Medan, Tomislav et al.

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Organizations seeking to utilize Large Language Models (LLMs) for knowledge querying and analysis often encounter challenges in maintaining an LLM fine-tuned on targeted, up-to-date information that keeps answers relevant and grounded.

Retrieval Augmented Generation (RAG) has quickly become a feasible solution for organizations looking to overcome the challenges of maintaining proprietary models and to help reduce LLM hallucinations in their query responses.
However, RAG comes with its own issues regarding scaling data pipelines across tiered-access and disparate data sources.
In many scenarios, it is necessary to query beyond a single data silo to provide richer and more relevant context for an LLM.

C-FedRAG: A Confidential Federated Retrieval-Augmented Generation System · Around

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Original
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., tau Yih, W., Rocktäschel, T., Riedel, S., and Kiela, D · 2005
Earlier work this paper cites.
A theoretical analysis of ndcg type ranking measures
Wang, Y., Wang, L., Li, Y., He, D., and Liu, T.-Y · 2013
Earlier work this paper cites.
Open algorithms for identity federation, 2017
Original
Hardjono, T. and Pentland, S · 2017
Earlier work this paper cites.
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Gautier Izacard, Mathilde Caron, L. H. S. R. P. B. A. J. E. G · 2021
Earlier work this paper cites.
Retrieval augmentation reduces hallucination in conversation, 2021
Original
Shuster, K., Poff, S., Chen, M., Kiela, D., and Weston, J · 2021
Earlier work this paper cites.
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Original
Roth, H. R., Cheng, Y., Wen, Y., Yang, I., Xu, Z., Hsieh, Y.-T., Kersten, K., Harouni, A., Zhao, C., Lu, K., Zhang, Z., Li, W., Myronenko, A., Yang, D., Yang, S., Rieke, N., Quraini, A., Chen, C., Xu, D., Ma, N., Dogra, P., Flores, M., and Feng, A · 2022
Earlier work this paper cites.
Common Terminology for Confidential Computing , 2022
Confidential Computing Consortium · 2023
Earlier work this paper cites.

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