Retrieval-augmented language models (RALMs) hold promise to produce language
understanding systems that are are factual, efficient, and up-to-date. An
important desideratum of RALMs, is that retrieved information helps model
performance when it is relevant, and does not harm performance when it is not.
This is particularly important in multi-hop reasoning scenarios, where misuse
of irrelevant evidence can lead to cascading errors. However, recent work has
shown that retrieval augmentation can sometimes have a negative effect on
performance. In this work, we present a thorough analysis on five open-domain
question answering benchmarks, characterizing cases when retrieval reduces
accuracy. We then propose two methods to mitigate this issue. First, a simple
baseline that filters out retrieved passages that do not entail question-answer
pairs according to a natural language inference (NLI) model. This is effective
in preventing performance reduction, but at a cost of also discarding relevant
passages. Thus, we propose a method for automatically generating data to
fine-tune the language model to properly leverage retrieved passages, using a
mix of relevant and irrelevant contexts at training time. We empirically show
that even 1,000 examples suffice to train the model to be robust to irrelevant
contexts while maintaining high performance on examples with relevant ones.

使用检索增强语言模型 (Retrieval-augmented language models, RALMs) 在处理特定应用场景问题时，检索到的信息能够提高模型的性能，并且不会损害性能。该研究分析了五个开放领域问答基准，并提出了两种方法以减轻性能下降问题。

提升检索增强语言模型对无关背景的鲁棒性

Making Retrieval-Augmented Language Models Robust to Irrelevant Context

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。