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URAG: A Benchmark for Uncertainty Quantification in Retrieval-Augmented Large Language Models

arXiv:2603.19281v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a widely adopted approach for enhancing LLMs in scenarios that demand extensive factual knowledge. However, current RAG evaluations concentrate primarily on correctness, which may not fully capture the impact of retrieval on LLM uncertainty and reliability. To bridge this gap, we introduce URAG, a comprehensive benchmark designed to assess the uncertainty of RAG systems across various fields like healthcare, programming, science, math, and general text. By reformulating open-ended generation tasks into multiple-choice question answering, URAG allows for principled uncertainty quantification via conformal prediction. We apply the evaluation pipeline to 8 standard RAG methods, measuring their performance through both accuracy and prediction-set sizes based on LAC and APS metrics. Our analysis shows that (1) accuracy gains often coincide with reduced uncertainty, but this relationship br

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Vinh Nguyen, Cuong Dang, Jiahao Zhang, Hoa Tran, Minh Tran, Trinh Chau, Thai Le, Lu Cheng, Suhang Wang · · 1 min read · 15 views
#cs.CL #cs.AI #cs.IR

arXiv:2603.19281v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a widely adopted approach for enhancing LLMs in scenarios that demand extensive factual knowledge. However, current RAG evaluations concentrate primarily on correctness, which may not fully capture the impact of retrieval on LLM uncertainty and reliability. To bridge this gap, we introduce URAG, a comprehensive benchmark designed to assess the uncertainty of RAG systems across various fields like healthcare, programming, science, math, and general text. By reformulating open-ended generation tasks into multiple-choice question answering, URAG allows for principled uncertainty quantification via conformal prediction. We apply the evaluation pipeline to 8 standard RAG methods, measuring their performance through both accuracy and prediction-set sizes based on LAC and APS metrics. Our analysis shows that (1) accuracy gains often coincide with reduced uncertainty, but this relationship breaks under retrieval noise; (2) simple modular RAG methods tend to offer better accuracy-uncertainty trade-offs than more complex reasoning pipelines; and (3) no single RAG approach is universally reliable across domains. We further show that (4) retrieval depth, parametric knowledge dependence, and exposure to confidence cues can amplify confident errors and hallucinations. Ultimately, URAG establishes a systematic benchmark for analyzing and enhancing the trustworthiness of retrieval-augmented systems. Our code is available on GitHub.

Executive Summary

This article introduces URAG, a novel benchmark for uncertainty quantification in Retrieval-Augmented Large Language Models (RAG). The authors reformulate open-ended generation tasks into multiple-choice question answering to evaluate the uncertainty of RAG systems across various domains. Through their analysis, they find that accuracy gains often coincide with reduced uncertainty, but this relationship breaks under retrieval noise. They also discover that simple modular RAG methods tend to offer better accuracy-uncertainty trade-offs than more complex reasoning pipelines. The authors conclude that no single RAG approach is universally reliable across domains and that retrieval depth, parametric knowledge dependence, and exposure to confidence cues can amplify confident errors and hallucinations. This study establishes a systematic benchmark for analyzing and enhancing the trustworthiness of retrieval-augmented systems.

Key Points

  • ▸ URAG is a comprehensive benchmark for uncertainty quantification in RAG systems.
  • ▸ The authors reformulate open-ended generation tasks into multiple-choice question answering to evaluate uncertainty.
  • ▸ Simple modular RAG methods tend to offer better accuracy-uncertainty trade-offs than more complex reasoning pipelines.

Merits

Strength in Methodology

The authors' use of conformal prediction allows for principled uncertainty quantification, making URAG a robust benchmark.

Demerits

Limitation in Generalizability

The study's findings may not generalize to other domains or RAG systems not evaluated in this study.

Expert Commentary

The article makes a significant contribution to the field of AI research by introducing a systematic benchmark for uncertainty quantification in RAG systems. The authors' use of conformal prediction is a particularly clever approach, allowing for principled uncertainty quantification. However, the study's findings may not generalize to other domains or RAG systems not evaluated in this study. Nevertheless, URAG has the potential to become a widely adopted benchmark in the AI community, enabling developers to create more trustworthy RAG systems. As such, policymakers should prioritize the development of trustworthy AI systems, such as those evaluated using URAG, to ensure their deployment in high-stakes applications.

Recommendations

  • ✓ Future studies should explore the application of URAG to other domains and RAG systems not evaluated in this study.
  • ✓ Developers of RAG systems should consider using URAG as a benchmark to evaluate their models' uncertainty and reliability.

Sources

Original: arXiv - cs.AI

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