Making Retrieval-Augmented Language Models Robust to Irrelevant Context

TL;DR

This paper analyzes the negative impact of irrelevant retrieved information on retrieval-augmented language models and proposes methods to improve their robustness, ensuring relevant information benefits performance without being harmed by irrelevant data.

Contribution

It introduces two techniques—NLI-based filtering and data augmentation with relevant and irrelevant contexts—to enhance RALMs' robustness against irrelevant retrievals.

Findings

Filtering out non-entailing passages prevents performance drops.

Fine-tuning with mixed relevant and irrelevant data improves robustness.

Models trained with 1,000 examples maintain high accuracy with irrelevant contexts.

Abstract

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.