Latest Research Papers
2024-07-01
arXiv
Searching for Best Practices in Retrieval-Augmented Generation
The paper investigates different RAG approaches to identify optimal practices that balance performance and efficiency, and shows that multimodal retrieval techniques can enhance question-answering and content generation.
Retrieval-augmented generation (RAG) techniques have proven to be effective
in integrating up-to-date information, mitigating hallucinations, and enhancing
response quality, particularly in specialized domains. While many RAG
approaches have been proposed to enhance large language models through
query-dependent retrievals, these approaches still suffer from their complex
implementation and prolonged response times. Typically, a RAG workflow involves
multiple processing steps, each of which can be executed in various ways. Here,
we investigate existing RAG approaches and their potential combinations to
identify optimal RAG practices. Through extensive experiments, we suggest
several strategies for deploying RAG that balance both performance and
efficiency. Moreover, we demonstrate that multimodal retrieval techniques can
significantly enhance question-answering capabilities about visual inputs and
accelerate the generation of multimodal content using a "retrieval as
generation" strategy.
2023-12-18
arXiv
Retrieval-Augmented Generation for Large Language Models: A Survey
The paper reviews Retrieval-Augmented Generation (RAG) for Large Language Models, which integrates external knowledge to improve accuracy and credibility. It covers the evolution of RAG paradigms and their components, and introduces a new evaluation framework. The paper also discusses current challenges and future research directions.
Large Language Models (LLMs) showcase impressive capabilities but encounter
challenges like hallucination, outdated knowledge, and non-transparent,
untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has
emerged as a promising solution by incorporating knowledge from external
databases. This enhances the accuracy and credibility of the generation,
particularly for knowledge-intensive tasks, and allows for continuous knowledge
updates and integration of domain-specific information. RAG synergistically
merges LLMs' intrinsic knowledge with the vast, dynamic repositories of
external databases. This comprehensive review paper offers a detailed
examination of the progression of RAG paradigms, encompassing the Naive RAG,
the Advanced RAG, and the Modular RAG. It meticulously scrutinizes the
tripartite foundation of RAG frameworks, which includes the retrieval, the
generation and the augmentation techniques. The paper highlights the
state-of-the-art technologies embedded in each of these critical components,
providing a profound understanding of the advancements in RAG systems.
Furthermore, this paper introduces up-to-date evaluation framework and
benchmark. At the end, this article delineates the challenges currently faced
and points out prospective avenues for research and development.