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New benchmark reveals MLLMs hallucinate text color over visual input

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new benchmark called Embedded Stroop to test how Multimodal Large Language Models (MLLMs) are affected by text embedded within images. This benchmark, utilizing the What-Color-Is-the-Text (WCIT) dataset, evaluates models on their ability to distinguish the color of text from the semantic meaning of the text itself when presented visually. While models show low accuracy in naming specific colors, they exhibit a significant tendency to hallucinate and report the embedded word's color instead of the actual text color, a phenomenon that can be reduced by altering the text's presentation. AI

IMPACT Highlights a specific vulnerability in MLLMs, potentially influencing future model development and evaluation methodologies.

RANK_REASON The cluster contains an academic paper detailing a new benchmark and dataset for evaluating MLLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark reveals MLLMs hallucinate text color over visual input

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The cluster contains an academic paper detailing a new benchmark and dataset for evaluating MLLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jinkun Zhao, Lei Huang, Haixin Ge, Wenjun Wu ·

    What Color Is the Text? A Benchmark for Hallucination Induced by Image-Embedded Prompt

    arXiv:2511.13400v3 Announce Type: replace Abstract: We introduce Embedded Stroop, a controlled diagnostic paradigm for measuring image-embedded prompt interference in Multimodal Large Language Models (MLLMs), where the query is rendered directly inside the visual input. Using the…

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