RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models
| Autor: | Hoshi, Yasuto, Miyashita, Daisuke, Ng, Youyang, Tatsuno, Kento, Morioka, Yasuhiro, Torii, Osamu, Deguchi, Jun |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. We open-source our code at https://github.com/yhoshi3/RaLLe. Comment: 18 pages, 2 figures, see https://youtu.be/JYbm75qnfTg for the demonstration screencast, accepted by EMNLP 2023 System Demonstrations |
| Databáze: | arXiv |
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