@inproceedings{patel-etal-2025-pcri,
title = "{PCRI}: Measuring Context Robustness in Multimodal Models for Enterprise Applications",
author = "Patel, Hitesh Laxmichand and
Agarwal, Amit and
Panda, Srikant and
Meghwani, Hansa and
Dua, Karan and
Li, Paul and
Sheng, Tao and
Ravi, Sujith and
Roth, Dan",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.14/",
pages = "195--214",
ISBN = "979-8-89176-333-3",
abstract = "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 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."
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<abstract>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 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.</abstract>
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%0 Conference Proceedings
%T PCRI: Measuring Context Robustness in Multimodal Models for Enterprise Applications
%A Patel, Hitesh Laxmichand
%A Agarwal, Amit
%A Panda, Srikant
%A Meghwani, Hansa
%A Dua, Karan
%A Li, Paul
%A Sheng, Tao
%A Ravi, Sujith
%A Roth, Dan
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F patel-etal-2025-pcri
%X 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 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.
%U https://aclanthology.org/2025.emnlp-industry.14/
%P 195-214
Markdown (Informal)
[PCRI: Measuring Context Robustness in Multimodal Models for Enterprise Applications](https://aclanthology.org/2025.emnlp-industry.14/) (Patel et al., EMNLP 2025)
ACL
- Hitesh Laxmichand Patel, Amit Agarwal, Srikant Panda, Hansa Meghwani, Karan Dua, Paul Li, Tao Sheng, Sujith Ravi, and Dan Roth. 2025. PCRI: Measuring Context Robustness in Multimodal Models for Enterprise Applications. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 195–214, Suzhou (China). Association for Computational Linguistics.