用扩散模型检测大模型幻觉，通过语义能量判断事实真伪。
The Energy of Falsehood: Detecting Hallucinations via Diffusion Model Likelihoods
- 将事实视为生成流形上的稳定吸引子，幻觉为不稳定状态。
- 在FEVER数据集上达到0.725的无监督AUROC，优于基线1.5个百分点。
- 无需标注即可零样本泛化，适用于跨领域事实验证场景。
大型语言模型常产生看似合理实则错误的陈述，传统不确定性度量在模型自信出错时难以察觉。本文提出DiffuTruth，一种基于非平衡热力学重构事实验证的无监督框架，认为真实事实是生成流形上的稳定吸引子，而幻觉则不稳定。引入生成应力测试：对陈述施加噪声后，用离散文本扩散模型重建。定义语义能量，通过NLI判别器衡量原陈述与重建之间的语义差异，可精准捕捉深层事实矛盾。进一步提出混合校准方法，融合该稳定性信号与判别置信度。在FEVER数据集上的实验表明，DiffuTruth取得0.725的无监督AUROC，较基线提升1.5个百分点，修正了过度自信预测。此外，在多跳推理的HOVER数据集上实现超4%的零样本性能提升，验证了热力学真实性质对分布偏移的鲁棒性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) frequently hallucinate plausible but incorrect assertions, a vulnerability often missed by uncertainty metrics when models are confidently wrong. We propose DiffuTruth, an unsupervised framework that reconceptualizes fact verification via non equilibrium thermodynamics, positing that factual truths act as stable attractors on a generative manifold while hallucinations are unstable. We introduce the Generative Stress Test, claims are corrupted with noise and reconstructed using a discrete text diffusion model. We define Semantic Energy, a metric measuring the semantic divergence between the original claim and its reconstruction using an NLI critic. Unlike vector space errors, Semantic Energy isolates deep factual contradictions. We further propose a Hybrid Calibration fusing this stability signal with discriminative confidence. Extensive experiments on FEVER demonstrate DiffuTruth achieves a state of the art unsupervised AUROC of 0.725, outperforming baselines by 1.5 percent through the correction of overconfident predictions. Furthermore, we show superior zero shot generalization on the multi hop HOVER dataset, outperforming baselines by over 4 percent, confirming the robustness of thermodynamic truth properties to distribution shifts.
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