# Exploring the Integration Strategies of Retriever and Large Language Models

**Authors:** Ye Liu, Semih Yavuz, Rui Meng, Meghana Moorthy, Shafiq Joty, Caiming Xiong, Yingbo Zhou  
**arXiv:** [2308.12574](https://arxiv.org/abs/2308.12574)  
**Date:** 2023-08-24  
**Summary page:** https://www.summarizepaper.com/en/paper/2308.12574v1/

## Key points

- Authors Liu, Yavuz, Meng, Moorthy, Joty, Xiong, and Zhou focus on integrating retrieved passages with large language models (LLMs) like ChatGPTs for open-domain question answering.
- The integration of retrieved passages with LLMs is challenging but essential for generating accurate responses by combining information effectively from different sources.
- The limitations of the commonly-used concatenation approach in integrating retrieved passages with LLMs are highlighted as it often leads to generating "unknown" outputs even when the correct document is among the top-k retrieved passages.
- To address this issue, the authors propose four alternative strategies for integrating retrieved passages with LLMs: two single-round methods and two multi-round approaches that incorporate feedback loops.
- Through comprehensive analyses and experiments, the authors provide valuable insights on leveraging retrieved passages efficiently to enhance answer generation using LLMs.
- By exploring different integration strategies and considering both single-round and multi-round approaches, the study aims to fill the gap in existing research and offer practical recommendations for improving open-domain question answering systems.

## Summary

In their paper titled "Exploring the Integration Strategies of Retriever and Large Language Models," authors Ye Liu, Semih Yavuz, Rui Meng, Meghana Moorthy, Shafiq Joty, Caiming Xiong, and Yingbo Zhou delve into the realm of open-domain question answering by focusing on the integration of retrieved passages with large language models (LLMs) like ChatGPTs. is a challenging task that requires effectively combining information from different sources to generate accurate responses. One promising approach is to integrate  with powerful , such as ChatGPTs. However, there is still a lack of exploration into the optimal methods for incorporating retrieved passages effectively. The authors start by highlighting the limitations of a commonly-used concatenation approach in integrating retrieved passages with LLMs. This often leads to generating "unknown" outputs even when the correct document is among the top-k retrieved passages. To address this issue, they propose four alternative strategies for integrating retrieved passages with LLMs. These strategies include two single-round methods that leverage  and two multi-round approaches that incorporate feedback loops. Through comprehensive analyses and experiments, Liu et al. provide valuable insights on how to leverage retrieved passages efficiently to enhance answer generation using LLMs. By exploring different  and considering both single-round and multi-round approaches, the authors aim to fill the gap in existing research and offer practical recommendations for improving open-domain question answering systems. Their study sheds light on the importance of thoughtful integration techniques in maximizing the potential of LLMs for generating accurate and relevant answers in response to user queries.