用最大池化提升幻觉检测效率，省去冗余计算仍保持高精度。
Max-pooling Network Revisited: Analyzing the Role of Semantic Probability in Multiple Instance Learning for Hallucination Detection
- 通过最大池化聚合词元特征，直接用轻量MLP预测句子得分。
- 相比现有方法减少重复采样与语义相似度计算，速度显著提升。
- 适合追求高效幻觉检测的系统集成者或部署场景使用。
幻觉检测对提升大语言模型可靠性至关重要。近期混合方法如HaMI结合语义一致性与内部模型状态，通过多实例学习（MIL）取得领先性能，但需反复采样和昂贵的语义相似度计算，带来巨大计算开销。本文从决策边界角度理论分析HaMI，发现将内部状态按语义一致性缩放可扩大决策边界。受此启发，我们从边界增大的视角重新审视经典句分类模型，采用最大池化聚合词元级特征，并通过轻量MLP直接估计句子得分。无需语义一致性计算，该方法在保持与最先进基线相当性能的同时，实现显著效率提升。代码已公开于https://github.com/FUJI1229/Hallucination_Detection。
原文摘要 · Abstract (English)
Hallucination detection has become increasingly important for improving the reliability of large language models (LLMs). Recently, hybrid approaches such as HaMI, which combine semantic consistency with internal model states via Multiple Instance Learning (MIL), have achieved state-of-the-art performance. However, these methods incur substantial computational overhead due to repeated sampling and costly semantic similarity computations. In this work, we first provide a theoretical analysis of HaMI in terms of decision margins, revealing that scaling internal states with semantic consistency leads to an enlarged decision margin. Motivated by this insight, we revisit classical sentence classification models from a margin enlargement perspective, aggregating token-level features via max pooling and directly estimating sentence scores using a lightweight MLP. Without requiring semantic consistency computations, our approach achieves substantial efficiency improvements while maintaining competitive performance with state-of-the-art baselines through adaptive aggregation of internal feature representations. Code is available at https://github.com/FUJI1229/Hallucination_Detection.
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