Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR)

Chenhao Fang; Jordi Mola; Mark Harman; Jason Nawrocki; Vaibhav Shrivastava; Yue Cheng; Jay Minesh Shah; Katayoun Zand; Mansi Tripathi; Arya Pudota; Matthew Becker; Hervé Robert; Abhishek Gulati

arXiv · 2604.11141

Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR)

Chenhao Fang·Jordi Mola·Mark Harman·Jason Nawrocki·Vaibhav Shrivastava·Yue Cheng·Jay Minesh Shah·Katayoun Zand·Mansi Tripathi·Arya Pudota·Matthew Becker·Hervé Robert·Abhishek Gulati

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Abstract

Although LLMs drive automation, it is critical to ensure immense consideration for high-stakes enterprise workflows such as those involving legal matters, risk management, and privacy compliance. For Meta, and other organizations like ours, a single hallucinated clause in such high stakes workflows risks material consequences. We show that by framing hallucination mitigation as a Minimum Bayes Risk (MBR) problem, we can dramatically reduce this risk. Specifically, we introduce a Hybrid Utility MBR (HUMBR) framework that synthesizes semantic embedding similarity with lexical precision to identify consensus without ground-truth references, for which we derive rigorous error bounds. We complement this theoretical analysis with a comprehensive empirical evaluation on widely-used public benchmark suites (TruthfulQA and LegalBench) and also real world data from Meta production deployment. The results from our empirical study show that MBR significantly outperforms standard Universal Self-Consistency. Notably, 81% of the pipeline's suggestions were preferred over human-crafted ground truth, and critical recall failures were virtually eliminated.

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Chenhao Fang, Jordi Mola, Mark Harman, Jason Nawrocki, Vaibhav Shrivastava, Yue Cheng, Jay Minesh Shah, Katayoun Zand, Mansi Tripathi, Arya Pudota, Matthew Becker, Hervé Robert, Abhishek Gulati. 2026-04-13. Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR). https://arxiv.org/abs/2604.11141

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