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.

本文旨在研究不同的方法将检索到的段落与大型语言模型相结合，以提高答案生成能力，并通过综合分析和实验证明了有效利用检索到的段落来增强大型语言模型答案生成能力的见解。

探索检索器和大型语言模型的整合策略

Exploring the Integration Strategies of Retriever and Large Language  Models

A straightforward approach to context-aware neural machine translation
consists in feeding the standard encoder-decoder architecture with a window of
consecutive sentences, formed by the current sentence and a number of sentences
from its context concatenated to it. In this work, we propose an improved
concatenation approach that encourages the model to focus on the translation of
the current sentence, discounting the loss generated by target context. We also
propose an additional improvement that strengthen the notion of sentence
boundaries and of relative sentence distance, facilitating model compliance to
the context-discounted objective. We evaluate our approach with both
average-translation quality metrics and contrastive test sets for the
translation of inter-sentential discourse phenomena, proving its superiority to
the vanilla concatenation approach and other sophisticated context-aware
systems.

本文提出一种改进的连接方法，加强句子边界和相对句子距离概念，从而促进模型满足上下文折扣化目标，提高语境感知神经机器翻译，表现优于基准连接法和其他复杂的上下文感知系统。