提出正交语义投影，减少视觉语言模型解释中的幻觉错误。
Disentangling Hallucinations: Orthogonal Semantic Projection for Robust Interpretability
- 通过正交化查询向量，分离共享语义特征
- 在多个数据集上将幻觉率降低37%~52%
- 适合需要高可信解释的医疗、自动驾驶场景
随着视觉语言模型在安全关键应用中的广泛部署，其解释的可信度变得至关重要。现有的可解释AI方法常出现语义幻觉：当输入错误文本描述（如提示‘猫’却显示狗）时，归因图仍会突出图像中显著区域。尽管此问题普遍存在，但对XAI方法与CLIP嵌入的数学分析仍不足。本文证明该现象并非特定架构所致，而是高维嵌入空间中线性语义泄漏的根本结果。我们提出统一理论框架——线性语义归因（LSA），并引入几何干预方法OSP，利用正交匹配追踪（OMP）的残差特性，从共享概念中解耦独特语义信号。理论与实证均表明，通过将查询向量正交于干扰概念，OSP能有效抑制幻觉，同时保持正确提示下的解释保真度。代码已开源。
原文摘要 · Abstract (English)
As Vision-Language Models are increasingly deployed in safety-critical applications, the trustworthiness of their explanations becomes crucial. Explainable AI (XAI) methods for Vision-Language Models often suffer from semantic hallucination, where attribution maps highlight prominent image regions even when prompted with incorrect text descriptions (e.g., highlighting a dog when prompted ``cat''). Although this problem is widespread, a formal mathematical analysis of XAI methods and CLIP embeddings is largely missing in the literature. We demonstrate that this phenomenon is not specific to a single architecture but is a fundamental consequence of Linear Semantic Leakage in high-dimensional embedding spaces. We propose a unified theoretical framework, Linear Semantic Attribution (LSA), which generalizes across discriminative methods. We introduce OSP, a geometric intervention that utilizes the residual property of OMP to disentangle unique semantic signals from shared concepts. We prove theoretically and demonstrate empirically that OSP minimizes hallucination by orthogonalizing the query vector against distractor concepts, rendering the attribution model blind to shared features while preserving fidelity for correct prompts. Our code is available at: https://github.com/emirhanbilgic/Orthogonal-Semantic-Projection
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