PCRI: Measuring Context Robustness in Multimodal Models for Enterprise Applications
This addresses the need for robust MLLMs in enterprise applications by providing a systematic evaluation metric, though it is incremental as it focuses on improving assessment rather than proposing a new model.
The paper tackled the problem of multimodal large language models (MLLMs) being sensitive to irrelevant visual context, which undermines reliability in real-world settings, by introducing the Patch Context Robustness Index (PCRI) to measure robustness; they found that most leading models remain brittle to background noise, with only a few like InternVL2-26B and Qwen2VL-72B showing consistent robustness across tasks.
The reliability of Multimodal Large Language Models (MLLMs) in real-world settings is often undermined by sensitivity to irrelevant or distracting visual context, an aspect not captured by existing evaluation metrics. We introduce the \textbf{Patch Context Robustness Index (PCRI)}, the first systematic and interpretable score for quantifying MLLM robustness to variations in visual context granularity, measuring performance changes between localized image patches and full-image input. Applying PCRI to 19 state-of-the-art MLLMs across 15 vision-language benchmarks, we find that most leading models remain brittle to background noise, with only a few, such as InternVL2-26B and Qwen2VL-72B, demonstrating consistent robustness across tasks. PCRI analysis also highlights how different model architectures handle and integrate visual context, offering actionable diagnostic insight for both researchers and practitioners. PCRI enables rigorous comparison of context robustness, supporting principled model selection and guiding the development of future architectures and training strategies for robust, real-world deployment.