Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation (2409.11598v3)

Abstract: Modern LLMs frequently include retrieval components to improve their outputs, giving rise to a growing number of retrieval-augmented generation (RAG) systems. Yet, most existing work in RAG has underemphasized fair ranking techniques and neglected the diverse interests of all stakeholders. In this paper, we present the first comprehensive study of RAG systems that incorporate fairness-aware rankings, focusing on both ranking fairness and attribution fairness - ensuring equitable exposure of sources cited in the final text. We specifically examine item-side fairness, i.e., whether retrieved documents receive balanced exposure, and assess how this affects both the system's overall performance and the eventual distribution of cited sources. Across twelve RAG models and seven tasks, we find that fairness-aware retrieval frequently retains or even improves ranking effectiveness and generation quality, countering the widespread belief that fairness compromises system performance. Moreover, we show that fair retrieval leads to more balanced attribution in the final responses, ensuring that the cited sources are credited more equitably. Our results underscore the importance of item-side fairness throughout both retrieval and generation phases, offering key insights for building more responsible and equitable RAG systems and illustrating promising avenues for future exploration in fair ranking and source attribution.

• The paper introduces a systematic evaluation of fair ranking in retrieval-augmented generation across nine models and seven datasets using Plackett-Luce sampling.
• It demonstrates that controlled fairness via the α parameter can enhance ranking and generation quality, challenging the typical tradeoff between fairness and performance.
• The study reveals that models like BM25, SPLADE, and Contriever achieve equitable exposure without sacrificing effectiveness, paving the way for more balanced NLP systems.

Insightful Overview of "Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation"

"Towards Fair RAG: On the Impact of Fair Ranking in Retrieval-Augmented Generation" by To Eun Kim and Fernando Diaz addresses a significant gap in current research on retrieval-augmented generation (RAG) systems. While the efficacy of RAG systems has been explored extensively, their fairness implications have often been overlooked. This paper provides the first systematic evaluation of RAG systems integrated with fair rankings, focusing on item-side fairness to ensure equitable exposure for all relevant items.

Core Contributions

The paper’s primary contributions lie in its detailed evaluation of nine RAG systems across seven datasets, examining the balance between item-fairness, ranking quality, and generation quality. The authors employ a stochastic ranking approach that leverages Plackett-Luce sampling to ensure that items are fairly exposed to the LLM, varying the sampling temperature parameter $\alpha$ to control the level of fairness.

Key Findings

1. Tradeoff Between Fairness and Ranking Quality:
   • The paper confirms that there is a general tradeoff between fairness and ranking quality. However, it also shows that this tradeoff is not as strict as previously assumed. For instance, models based on SPLADE and Contriever can maintain high ranking quality while ensuring fairness.
   • Surprisingly, BM25-based models exhibited improved ranking quality with increased fairness up to a point, challenging the assumption that deterministic rankers always provide superior relevance.
2. Impact on Generation Quality:
   • A strong correlation was observed between ranking quality based on utility labels and the generation quality of the RAG systems. This highlights the importance of moving from relevance-based to utility-based evaluations in the context of RAG, where the consumer is a machine.
   • Most notably, integrating fair rankings into RAG often led to better or at least non-degraded generation quality compared to traditional deterministic rankings. This finding holds significant implications for the practical deployment of RAG systems, suggesting that fairness need not come at the cost of effectiveness.

Methodology

Kim and Diaz used a comprehensive experimental setup. They incorporated well-known retrieval models like BM25, SPLADE, and Contriever, sampled rankings stochastically, and measured output utility using the Flan-T5 family of generative models at different scales. By varying the $\alpha$ parameter, they could produce rankings with different levels of fairness and observe the resulting impact on both ranking and generation quality.

To quantify fairness and ranking quality, they used expected exposure disparity (EE-D) and expected exposure relevance (EE-R) metrics. These metrics enabled the researchers to analyze how well the system distributes attention across useful items and how this distribution aligns with an ideal, fairness-oriented exposure.

Implications and Future Directions

• Practical Implications:
  • The findings suggest that RAG models can achieve high-quality, fair outcomes simultaneously. This has practical implications for any industry reliant on NLP systems, where content providers must be equitably rewarded. For instance, in recommendation systems or information retrieval engines, adopting such fairness-aware RAG models could lead to more balanced and fair content exposure without significant performance degradation.
• Theoretical Implications:
  • On a theoretical level, this work challenges the conventional understanding of the fairness-efficiency tradeoff in machine learning systems. It underscores the need for more nuanced metrics that go beyond precision and recall to incorporate utility-based fairness.
• Future Research Directions:
  • There’s room for further exploration on more sophisticated machine-user browsing models. The current model assumes equal attention to all top-k items, but more refined models could account for varying attention distributions, potentially resulting in even fairer and more effective RAG systems.
  • Future studies could also focus on exploring graded utility judgments and different notions of fairness, such as group fairness, to broaden the understanding of fair exposure in diverse contexts.

Conclusion

Kim and Diaz’s work provides essential insights into developing fair and effective RAG systems. By demonstrating that high standards of fairness can be achieved without severely compromising, and sometimes even enhancing, system performance, their research lays foundational groundwork for future explorations into fair NLP systems. This paper is a vital contribution to the discourse on fairness in AI and opens up new avenues for both theoretical exploration and practical application in the rapidly evolving field of retrieval-augmented generation.