We present a novel approach to automatically generate non-trivial task-specific synthetic datasets for hallucination detection. Our approach features a two-step generation-selection pipeline, using hallucination pattern guidance and a language style alignment during generation. Hallucination pattern guidance leverages the most important task-specific hallucination patterns while language style alignment aligns the style of the synthetic dataset with benchmark text. To obtain robust supervised detectors from synthetic datasets, we also adopt a data mixture strategy to improve performance robustness and generalization. Our results on three datasets show that our generated hallucination text is more closely aligned with non-hallucinated text versus baselines, to train hallucination detectors with better generalization. Our hallucination detectors trained on synthetic datasets outperform in-context-learning (ICL)-based detectors by a large margin of 32%. Our extensive experiments confirm the benefits of our approach with cross-task and cross-generator generalization. Our data-mixture-based training further improves the generalization and robustness of hallucination detection.

翻译：本文提出了一种新颖的方法，用于自动生成非平凡的、任务特定的合成数据集以进行幻觉检测。我们的方法采用两步生成-选择流程，在生成过程中利用幻觉模式引导和语言风格对齐。幻觉模式引导利用了最重要的任务特定幻觉模式，而语言风格对齐则将合成数据集的风格与基准文本对齐。为了从合成数据集中获得鲁棒的监督检测器，我们还采用了数据混合策略以提高性能的鲁棒性和泛化能力。我们在三个数据集上的结果表明，与基线方法相比，我们生成的幻觉文本与非幻觉文本的匹配度更高，从而能够训练出具有更好泛化能力的幻觉检测器。在合成数据集上训练的幻觉检测器，其性能大幅优于基于上下文学习（ICL）的检测器，优势达到32%。我们的大量实验证实了该方法在跨任务和跨生成器泛化方面的优势。我们基于数据混合的训练进一步提高了幻觉检测的泛化能力和鲁棒性。