Modeling Uncertainty and Using Post-fusion as Fallback Improves Retrieval Augmented Generation with LLMs

TL;DR

This paper investigates methods to improve retrieval-augmented generation with LLMs by modeling uncertainty and employing fallback strategies, leading to more accurate and reliable answer generation.

Contribution

It introduces alternative passage integration strategies and demonstrates their effectiveness over the standard concatenation approach in retrieval-augmented LLMs.

Findings

Concatenation often leads to 'unknown' outputs despite correct passages being retrieved.

Alternative strategies with reasoning and feedback improve answer accuracy.

Modeling uncertainty and fallback methods enhance LLM performance.

Abstract

The integration of retrieved passages and large language models (LLMs), such as ChatGPTs, has significantly contributed to improving open-domain question answering. However, there is still a lack of exploration regarding the optimal approach for incorporating retrieved passages into the answer generation process. This paper aims to fill this gap by investigating different methods of combining retrieved passages with LLMs to enhance answer generation. We begin by examining the limitations of a commonly-used concatenation approach. Surprisingly, this approach often results in generating "unknown" outputs, even when the correct document is among the top-k retrieved passages. To address this issue, we explore four alternative strategies for integrating the retrieved passages with the LLMs. These strategies include two single-round methods that utilize chain-of-thought reasoning and two multi-round strategies that incorporate feedback loops. Through comprehensive analyses and experiments, we provide insightful observations on how to effectively leverage retrieved passages to enhance the answer generation capability of LLMs.