用引导式生成与风格对齐，自动造出更逼真的幻觉检测数据。
Controlled Automatic Task-Specific Synthetic Data Generation for Hallucination Detection
- 分两步生成：先用幻觉模式引导，再对齐语言风格。
- 在三个数据集上生成的文本更接近真实文本，检测器性能提升32%。
- 适合需要高泛化能力幻觉检测的研究者与应用开发者。
我们提出一种新颖方法，自动生成非平凡的任务特定合成数据集以用于幻觉检测。该方法采用两阶段生成-筛选流程，在生成过程中结合幻觉模式引导与语言风格对齐。幻觉模式引导利用关键任务相关幻觉模式，语言风格对齐则使合成数据的语言风格与基准文本一致。为获得鲁棒的监督检测器，我们还引入数据混合策略以增强性能稳健性与泛化能力。在三个数据集上的实验表明，我们生成的幻觉文本相较于基线更贴近非幻觉文本，训练出的检测器具备更强泛化能力。基于合成数据训练的检测器相比基于上下文学习（ICL）的检测器性能高出32%。大量实验验证了本方法在跨任务与跨生成器场景下的有效性，数据混合训练进一步提升了检测的泛化性与鲁棒性。
原文摘要 · Abstract (English)
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.
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