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.

翻译：尽管大型语言模型（LLM）推动了自动化进程，但在涉及法律事务、风险管理及隐私合规等高风险企业工作流中，确保对其结果的审慎考量至关重要。对于Meta及类似组织而言，此类高风险工作流中一个虚构的条款便可能引发实质性后果。研究表明，将幻觉抑制问题建模为最小贝叶斯风险（MBR）问题，可显著降低此类风险。具体而言，我们提出了混合效用最小贝叶斯风险（HUMBR）框架，该框架通过融合语义嵌入相似性与词汇精确性，可在无真实参考标准的情况下识别共识结果，并为此推导了严格的误差界限。我们通过广泛使用的公共基准测试套件（TruthfulQA和LegalBench）以及Meta生产环境的真实数据，对该理论分析进行了全面的实证验证。实验结果表明，MBR方法显著优于标准通用自一致性方法。值得注意的是，该流程中81%的建议优于人工标注的真实结果，关键召回错误几乎被完全消除。