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.

本研究针对幻觉检测中的任务特定合成数据不足问题，提出了一种新颖的自动生成方法。通过采用幻觉模式引导和语言风格一致性对生成过程进行优化，研究表明所生成的合成文本与真实文本的对齐更好，从而提高幻觉探测器的泛化能力，检测效果比现有方法高出32%。

用于幻觉检测的受控自动任务特定合成数据生成