IRB: Automated Generation of Robust Factuality Benchmarks

• URL: http://arxiv.org/abs/2602.08070v1
• Date: Sun, 08 Feb 2026 18:01:43 GMT
• Title: IRB: Automated Generation of Robust Factuality Benchmarks
• Authors: Lam Thanh Do, Bhagyashree Taleka, Hozaifa Ammar Bhutta, Vikram Sharma Mailthody, Kevin Chen-Chuan Chang, Wen-mei Hwu,
• Abstract summary: IRB is a framework for automatically generating benchmarks to evaluate the factuality of RAG systems.<n>We utilize IRB to construct a benchmark and evaluate frontier LLMs and retrievers.
• Score: 21.23127687776739
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Static benchmarks for RAG systems often suffer from rapid saturation and require significant manual effort to maintain robustness. To address this, we present IRB, a framework for automatically generating benchmarks to evaluate the factuality of RAG systems. IRB employs a structured generation pipeline utilizing \textit{factual scaffold} and \textit{algorithmic scaffold}. We utilize IRB to construct a benchmark and evaluate frontier LLMs and retrievers. Our results demonstrate that IRB poses a significant challenge for frontier LLMs in the closed-book setting. Furthermore, our evaluation suggests that reasoning LLMs are more reliable, and that improving the retrieval component may yield more cost-effective gains in RAG system correctness than scaling the generator.

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