Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems (2409.19804v2)

Abstract: Retrieval-Augmented Generation (RAG) has recently gained significant attention for its enhanced ability to integrate external knowledge sources into open-domain question answering (QA) tasks. However, it remains unclear how these models address fairness concerns, particularly with respect to sensitive attributes such as gender, geographic location, and other demographic factors. First, as LLMs evolve to prioritize utility, like improving exact match accuracy, fairness considerations may have been largely overlooked. Second, the complex, multi-component architecture of RAG methods poses challenges in identifying and mitigating biases, as each component is optimized for distinct objectives. In this paper, we aim to empirically evaluate fairness in several RAG methods. We propose a fairness evaluation framework tailored to RAG, using scenario-based questions and analyzing disparities across demographic attributes. Our experimental results indicate that, despite recent advances in utility-driven optimization, fairness issues persist in both the retrieval and generation stages. These findings underscore the need for targeted interventions to address fairness concerns throughout the RAG pipeline. The dataset and code used in this study are publicly available at this GitHub Repository https://github.com/elviswxy/RAG_fairness .

• The paper introduces a fairness evaluation framework for RAG systems using demographic-sensitive scenarios.
• Empirical analysis reveals that high utility often comes with increased bias, especially in naive and large retriever models.
• Component-level insights highlight that retrieval and refinement stages are critical leverage points for mitigating bias.

Evaluating Fairness in Retrieval-Augmented Generation Systems for LLMs

The paper "Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems" presents an empirical investigation into fairness concerns inherent in Retrieval-Augmented Generation (RAG) methods employed in LLMs. Given the increased adoption of RAG techniques in augmenting LLMs with external knowledge sources, it is crucial to address the potential for these systems to propagate or exacerbate biases, especially concerning sensitive demographic attributes like gender and geographic location.

Key Contributions

• Fairness Evaluation Framework: The authors propose a systematic fairness evaluation framework tailored specifically for RAG systems. This framework incorporates scenario-based questions, leveraging datasets such as TREC 2022 Fair Ranking Track and BBQ, to explore the impact of sensitive demographic attributes on the fairness of RAG-generated outputs.
• Empirical Analysis: The paper evaluates multiple RAG methods, including Naive, Selective-Context, SKR, FLARE, and Iter-RetGen, employing metrics like Exact Match (EM), ROUGE-1, Group Disparity (GD), and Equalized Odds (EO). Their analysis reveals a persistent trade-off between utility (accuracy) and fairness in RAG methods.
• Component-Level Insights: By decomposing the RAG pipeline into retriever, refiner, judger, and generator components, the authors highlight the different contributions of each stage to the overall bias in the system.

Findings

1. Utility vs. Fairness Trade-Off: Across various RAG methods, improvements in utility metrics like EM do not correspond to enhancements in fairness. For example, the Naive method, while exhibiting high EM scores, also shows significant biases. Conversely, while fairness-focused methods like Iter-RetGen mitigate some biases, they often do so at the cost of reduced accuracy.
2. Retriever Impact: The choice of retrieval model significantly influences fairness. The paper demonstrates that dense retrievers (e.g., E5-base) generally show more balanced outcomes compared to sparse retrievers (e.g., BM25). However, larger models like E5-large introduce greater biases, favoring male-related content due to their higher MRR (Mean Reciprocal Rank) for such documents.
3. Effectiveness of Refinement: Refinement processes, such as those implemented in Selective-Context and Iter-RetGen, show minimal impact on fairness. These methods refine retrieval outputs to focus on highly relevant content, but they still reflect the biases present in the initial retrieval steps.
4. Judger and Generator Contribution: Incorporating a judger component does not significantly alter fairness, though specific judger decisions (e.g., in FLARE) can contribute to increased bias. Larger generator models (e.g., Meta-Llama-3-70B-Instruct) show better performance in fairness metrics but introduce trade-offs with EM.

Practical Implications and Future Directions

The persistence of fairness issues across various RAG methods underscores the need for more targeted interventions. Practical implications include:

• Prioritizing Document Relevance: Adjusting the ranking of retrieved documents to prioritize relevant content from protected groups can mitigate biases effectively. This approach decreases the unfairness metrics while potentially improving accuracy.
• Fine-Tuning Retrieval Strategies: Developing fairness-aware retrieval mechanisms that balance relevance and demographic representation could address biases at the source.
• Advancing Evaluation Frameworks: The paper's proposed scenario-based evaluation framework could be extended to include additional demographic factors and more complex scenarios to better capture the nuances of fairness in RAG systems.

Future research should explore advanced methods for mitigating biases in RAG systems, such as incorporating fairness constraints into model training or leveraging adversarial techniques to debias retrieval outputs. Additionally, further studies should generalize the findings to a broader range of datasets and contexts, ensuring robustness across diverse applications.

Conclusion

This paper provides a comprehensive analysis of fairness concerns in RAG methods for LLMs. By highlighting the trade-offs between utility and fairness and examining the contributions of different components in the RAG pipeline, the authors offer valuable insights for developing more equitable AI systems. The proposed mitigation strategies, coupled with a robust evaluation framework, set the stage for future research aimed at achieving both high performance and fairness in real-world applications.