AINeutralarXiv – CS AI · 3h ago6/10
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Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers
Researchers evaluated how multimodal large language models (MLLMs) explain their image classification decisions in few-shot learning scenarios. The study found that forcing models to generate formal, concept-based explanations actually reduces their predictive accuracy from 93.8% to 90.1%, suggesting that explicit reasoning doesn't universally improve performance despite being widely assumed to do so.