retrieval-augmented language models have exhibited promising performance
across various areas of natural language processing (NLP), including
fact-critical tasks. However, due to the black-box nature of advanced
R-LLMs improve factual question-answering by combining pre-trained large language models with retrieval systems; RaLLe is an open-source framework that facilitates the development, evaluation, and optimization of R-LLMs for knowledge-intensive tasks, enhancing performance and accuracy.