Evian：迈向可解释的视觉指令微调数据审计
Evian: Towards Explainable Visual Instruction-tuning Data Auditing
AI总结：
本文提出Evian框架，通过分解评估模型响应的三大认知组件，解决数据审计中逻辑一致性与事实准确性不足的问题，验证高质量小规模数据优于大规模低质量数据。
摘要：
大型视觉-语言模型（LVLMs）的效果严重依赖于训练数据质量，需在视觉真实性和指令遵循能力间取得平衡。现有数据集质量不一致，现有过滤方法缺乏识别逻辑谬误或事实错误的粒度。为此，我们提出三个贡献：首先构建包含300,000样本的基准测试集，系统注入多样细微缺陷；其次引入
英文摘要：
The efficacy of Large Vision-Language Models (LVLMs) is critically dependent on the quality of their training data, requiring a precise balance between visual fidelity and instruction-following capability. Existing datasets, however, are plagued by inconsistent quality, and current data filtering methods rely on coarse-grained scores that lack the granularity to identify nuanced semantic flaws like logical fallacies or factual errors. This creates a fundamental bottleneck in developing more reliable models. To address this, we make three core contributions. First, we construct a large-scale, 300K-sample benchmark by systematically injecting diverse, subtle defects to provide a challenging testbed for data auditing. Second, we introduce a novel "Decomposition-then-Evaluation" paradigm that breaks model responses into constituent cognitive components: visual description, subjective inference, and factual claim, enabling targeted analysis. Third, we instantiate this paradigm via EVIAN (Explainable Visual Instruction-tuning Data AuditiNg), an automated framework that evaluates these components along the orthogonal axes of Image-Text Consistency, Logical Coherence, and Factual Accuracy. Our empirical findings challenge the prevailing scale-centric paradigm: a model fine-tuned on a compact, high-quality subset curated by EVIAN consistently surpassed models trained on orders-of-magnitude larger datasets. We also reveal that dividing complex auditing into verifiable subtasks enables robust curation, and that Logical Coherence is the most critical factor in data quality evaluation.
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