首次用信息论量化多模态模型在分布偏移下的风险，揭示其可靠性瓶颈。
Understanding Multimodal LLMs Under Distribution Shifts: An Information-Theoretic Approach
- 引入有效互信息（EMI）衡量输入与输出的相关性，量化模型风险。
- 理论推导出分布偏移下风险的上限，与视觉和文本差异相关。
- 61种真实场景测试验证理论，为模型安全应用提供依据。
多模态大语言模型（MLLMs）虽具潜力，但在分布偏移下表现不佳，即评估数据与指令微调数据分布不一致。现有工作多为实证分析，我们主张建立形式化框架以刻画并量化MLLMs在分布偏移下的风险，确保其在真实世界中的安全可靠应用。本文从信息论视角出发，提出首个可量化分布偏移下最大风险的理论框架。核心是引入有效互信息（EMI），一种系统性度量输入查询与模型响应间相关性的指标。我们推导出ID（内分布）与OOD（外分布）数据间EMI差异的上界，将其与视觉与文本分布差异相联系。在61种真实分布偏移场景下，基于多个基准数据集的广泛实验验证了理论洞见。
原文摘要 · Abstract (English)
Multimodal large language models (MLLMs) have shown promising capabilities but struggle under distribution shifts, where evaluation data differ from instruction tuning distributions. Although previous works have provided empirical evaluations, we argue that establishing a formal framework that can characterize and quantify the risk of MLLMs is necessary to ensure the safe and reliable application of MLLMs in the real world. By taking an information-theoretic perspective, we propose the first theoretical framework that enables the quantification of the maximum risk of MLLMs under distribution shifts. Central to our framework is the introduction of Effective Mutual Information (EMI), a principled metric that quantifies the relevance between input queries and model responses. We derive an upper bound for the EMI difference between in-distribution (ID) and out-of-distribution (OOD) data, connecting it to visual and textual distributional discrepancies. Extensive experiments on real benchmark datasets, spanning 61 shift scenarios, empirically validate our theoretical insights.
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