Abstract
Techniques are provided to enhance the functions of a retrieval augmented generation (RAG) mechanism for a large language model (LLM). A Federated Learning (FL)-enhanced RAG (FLERAG) mechanism is provided that can account for relevant context-enhancing data from the retrieval process, as well as most recent data from the FL on which a large language model (LLM) may not have been trained. Using FLERAG, the output generation is determined through a scoring or ranking method that indicates whether the response from the LLM or the FL model is most accurate and relevant. This generated response is then provided back to a user.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Kim, Eugenia; Holst, Amanda L. Ph.D.; and Lee, Myungjin, "FEDERATED LEARNING-ENHANCED RETRIEVAL AUGMENTED GENERATION (RAG)", Technical Disclosure Commons, (June 24, 2024)
https://www.tdcommons.org/dpubs_series/7122