通过分析生成过程中的隐藏证据，精准识别扩散型大模型的幻觉。
HIVE: Hidden-Evidence Verification for Hallucination Detection in Diffusion Large Language Models
- 从去噪轨迹中提取压缩的隐藏证据，选择关键步骤与层的信息
- 在三个问答数据集上最高达0.9236的AUROC和0.9537的AUPRC
- 适合关注大模型可信生成与幻觉检测的研究者
扩散型大语言模型通过多步去噪生成文本，幻觉信号可能在整个生成轨迹中出现，而不仅存在于最终输出。现有检测方法主要依赖输出不确定性或粗粒度轨迹统计，难以捕捉D-LLMs更丰富的隐藏动态。我们提出HIVE框架，从去噪轨迹中提取压缩的隐藏证据，选择信息量大的步骤-层证据，并通过前缀嵌入将这些证据作为条件输入验证语言模型。HIVE不仅能生成连续的幻觉得分（基于验证器决策逻辑），还能输出结构化结果，包括幻觉类型、证据对及简短推理。在两个D-LLM和三个QA基准上，HIVE持续优于八种强基线，最高达到0.9236 AUROC与0.9537 AUPRC。消融实验证实了隐藏证据条件、学习式证据选择、双流证据表示与步骤-层嵌入的重要性。结果表明，从去噪轨迹中选取的隐藏证据，比仅依赖输出不确定性的方法提供更强且更可用的幻觉信号。
原文摘要 · Abstract (English)
Diffusion large language models generate text through multi-step denoising, where hallucination signals may emerge throughout the trajectory rather than only in the final output. Existing detectors mainly rely on output uncertainty or coarse trace statistics, which often fail to capture the richer hidden dynamics of D-LLMs. We propose HIVE, a hidden-evidence verification framework that extracts compressed hidden evidence from denoising trajectories, selects informative step-layer evidence, and conditions a verifier language model on the selected evidence through prefix embeddings. HIVE produces both a continuous hallucination score from verifier decision logits and structured verification outputs, including hallucination types, evidence pairs, and short rationales. Across two D-LLMs and three QA benchmarks, HIVE consistently outperforms eight strong baselines and achieves up to 0.9236 AUROC and 0.9537 AUPRC. Ablation studies further confirm the importance of hidden-evidence conditioning, learned evidence selection, two-stream evidence representation, and step-layer embeddings. These results suggest that selected hidden evidence from denoising trajectories provides a stronger and more usable hallucination signal than output-only uncertainty or coarse trace statistics.
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