RaLLe: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models
This work addresses a specific problem for developers of R-LLMs by providing tools for better evaluation and optimization, though it is incremental as it builds on existing R-LLM concepts.
The authors tackled the lack of transparency in evaluating and optimizing prompts for retrieval-augmented large language models (R-LLMs) by developing RaLLe, an open-source framework that facilitates development and evaluation, leading to improved performance and accuracy in knowledge-intensive tasks.
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.