RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. However, even the most advanced LLMs face challenges such as hallucinations and real-time updating of their knowledge. Current research addresses this bottleneck by equipping LLMs with...
Saved in:
Main Authors | , , , , , , , , , , , , |
---|---|
Format | Journal Article |
Language | English |
Published |
21.08.2024
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Large Language Models (LLMs) demonstrate human-level capabilities in
dialogue, reasoning, and knowledge retention. However, even the most advanced
LLMs face challenges such as hallucinations and real-time updating of their
knowledge. Current research addresses this bottleneck by equipping LLMs with
external knowledge, a technique known as Retrieval Augmented Generation (RAG).
However, two key issues constrained the development of RAG. First, there is a
growing lack of comprehensive and fair comparisons between novel RAG
algorithms. Second, open-source tools such as LlamaIndex and LangChain employ
high-level abstractions, which results in a lack of transparency and limits the
ability to develop novel algorithms and evaluation metrics. To close this gap,
we introduce RAGLAB, a modular and research-oriented open-source library.
RAGLAB reproduces 6 existing algorithms and provides a comprehensive ecosystem
for investigating RAG algorithms. Leveraging RAGLAB, we conduct a fair
comparison of 6 RAG algorithms across 10 benchmarks. With RAGLAB, researchers
can efficiently compare the performance of various algorithms and develop novel
algorithms. |
---|---|
DOI: | 10.48550/arxiv.2408.11381 |