Yasuto Hoshi


2023

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RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models
Yasuto Hoshi | Daisuke Miyashita | Youyang Ng | Kento Tatsuno | Yasuhiro Morioka | Osamu Torii | Jun Deguchi
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Retrieval-augmented large language models (R-LLMs) combine pre-trained large language models (LLMs) with information retrieval systems to improve the accuracy of factual question-answering. However, current libraries for building R-LLMs provide high-level abstractions without sufficient transparency for evaluating and optimizing prompts within specific inference processes such as retrieval and generation. To address this gap, we present RaLLe, an open-source framework designed to facilitate the development, evaluation, and optimization of R-LLMs for knowledge-intensive tasks. With RaLLe, developers can easily develop and evaluate R-LLMs, improving hand-crafted prompts, assessing individual inference processes, and objectively measuring overall system performance quantitatively. By leveraging these features, developers can enhance the performance and accuracy of their R-LLMs in knowledge-intensive generation tasks.