# RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit

**Authors:** Jiongnan Liu, Jiajie Jin, Zihan Wang, Jiehan Cheng, Zhicheng Dou, Ji-Rong Wen  
**arXiv:** [2306.05212](https://arxiv.org/abs/2306.05212)  
**Date:** 2023-06-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2306.05212v1/

## Key points

- Large Language Models (LLMs) are popular for generating detailed responses to user inputs
- However, LLMs can generate fictitious responses or hallucinations
- Retrieval-augmented LLMs incorporate external knowledge from relevant content retrieved by Information Retrieval (IR) systems to generate more factual texts in response to user input
- RETA-LLM is a toolkit that facilitates the development of retrieval-augmented LLM systems and offers more plug-and-play modules than previous systems
- RETA-LLM includes request rewriting, document retrieval, passage extraction, answer generation, and fact checking modules
- LLMs are capable of generating high-quality responses that are informative and polite with tremendous world knowledge stored in parameters and Reinforcement Learning from Human Feedback techniques incorporated into them
- LangChain is an advanced language system powered by RETA-LLM designed to provide users with accurate answers based on relevant information extracted from multiple sources.

## Summary

Large Language Models (LLMs) have become increasingly popular in various domains due to their ability to generate helpful and detailed responses to user inputs. However, LLMs still have a tendency to generate fictitious responses or hallucinations, which can be problematic. To alleviate this issue, researchers have proposed augmenting LLMs with Information Retrieval (IR) systems, also known as retrieval-augmented LLMs. By incorporating external knowledge from relevant content retrieved by IR systems from external corpora as references, retrieval-augmented LLMs can generate more factual texts in response to user input. Additionally, they can answer in-domain questions that cannot be answered solely by relying on the world knowledge stored in parameters. To facilitate the development of retrieval-augmented LLM systems and support research in this area, a team of researchers has developed RETA-LLM, a Retrieval-Augmented Large Language Model Toolkit. The toolkit provides a complete pipeline for building customized in-domain LLM-based systems and offers more plug-and-play modules than previous retrieval-augmented LLM systems. These modules include request rewriting, document retrieval, passage extraction, answer generation and fact checking. With tremendous world knowledge stored in parameters and Reinforcement Learning from Human Feedback techniques incorporated into them, LLMs are capable of generating high quality responses that are both informative and polite. RETA-LLM takes this capability further by enabling the creation of an advanced language system powered by it. This system is called LangChain and is designed to provide users with accurate answers based on relevant information extracted from multiple sources. Overall, RETA-LLM represents a significant step forward for the development of retrieval augmented LLM systems. Its comprehensive pipeline and plug and play modules make it easier for researchers and users alike to build customized language models that incorporate external knowledge effectively.