Evaluating the Effect of Retrieval Augmentation on Social Biases

TL;DR

This study investigates how Retrieval Augmented Generation (RAG) influences social biases in language models across multiple languages and bias types, revealing that RAG can amplify existing biases in generated text.

Contribution

The paper systematically analyzes the impact of RAG components on social biases in multilingual NLG, highlighting potential amplification of biases and raising awareness for responsible deployment.

Findings

Biases in document collections are often amplified in RAG-generated responses.

Even low-bias LLMs can produce responses with significant social biases when using biased document collections.

RAG's influence on social biases varies across languages and bias types.

Abstract

Retrieval Augmented Generation (RAG) has gained popularity as a method for conveniently incorporating novel facts that were not seen during the pre-training stage in Large Language Model (LLM)-based Natural Language Generation (NLG) systems. However, LLMs are known to encode significant levels of unfair social biases. The modulation of these biases by RAG in NLG systems is not well understood. In this paper, we systematically study the relationship between the different components of a RAG system and the social biases presented in the text generated across three languages (i.e. English, Japanese and Chinese) and four social bias types (i.e. gender, race, age and religion). Specifically, using the Bias Question Answering (BBQ) benchmark datasets, we evaluate the social biases in RAG responses from document collections with varying levels of stereotypical biases, employing multiple LLMs used as generators. We find that the biases in document collections are often amplified in the generated responses, even when the generating LLM exhibits a low-level of bias. Our findings raise concerns about the use of RAG as a technique for injecting novel facts into NLG systems and call for careful evaluation of potential social biases in RAG applications before their real-world deployment.