CBR-RAG: case-based reasoning for retrieval augmented generation in LLMs for legal question answering.
(2024)
Presentation / Conference Contribution
WIRATUNGA, N., ABEYRATNE, R., JAYAWARDENA, L., MARTIN, K., MASSIE, S., NKISI-ORJI, I., WEERASINGHE, R., LIRET, A. and FLEISCH, B. 2024. CBR-RAG: case-based reasoning for retrieval augmented generation in LLMs for legal question answering. In Recio-Garcia, J.A., Orozco-del-Castillo, M.G. and Bridge, D (eds.) Case-based reasoning research and development: proceedings of the 32nd International conference of case-based reasoning research and development 2024 (ICCBR 2024), 1-4 July 2024, Merida, Mexico. Lecture notes in computer science, 14775. Cham: Springer [online], pages 445-460. Available from: https://doi.org/10.1007/978-3-031-63646-2_29
Retrieval-Augmented Generation (RAG) enhances Large Language Model (LLM) output by providing prior knowledge as context to input. This is beneficial for knowledge-intensive and expert reliant tasks, including legal question-answering, which require e... Read More about CBR-RAG: case-based reasoning for retrieval augmented generation in LLMs for legal question answering..