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MLLMs tested as human surrogates for Explainable AI evaluation

By PulseAugur Editorial · [1 sources] ·

Researchers have explored the use of Multimodal Large Language Models (MLLMs) as a substitute for human evaluation in the field of Explainable AI (XAI). A new framework was developed to measure explanation utility for both MLLMs and humans, aiming to reduce the cost and improve the scalability of XAI evaluation. Empirical testing across fourteen popular MLLMs indicated that models from OpenAI and Qwen demonstrated higher alignment, suggesting their potential as reliable surrogates for human testing in XAI research. AI

IMPACT Potential to streamline and scale the evaluation of AI explainability methods, reducing reliance on costly human studies.

RANK_REASON Research paper proposing a new evaluation framework for XAI using MLLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

MLLMs tested as human surrogates for Explainable AI evaluation

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Research paper proposing a new evaluation framework for XAI using MLLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Eoin M. Kenny, Salim I. Amoukou, Tom Bewley ·

    Beyond Proxy Metrics: MLLMs as Human Surrogates for Explainable AI Evaluation

    arXiv:2611.13926v1 Announce Type: new Abstract: Explainable AI (XAI) has expanded into a broad and impactful research area, but the fundamental question of how best to evaluate explanation techniques has proven frustratingly elusive. Methodologies typically revolve around either …

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