Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers
📰 ArXiv cs.AI
Evaluating concept-based explanations of MLLMs as ICL visual classifiers reveals that explaining is harder than predicting alone
Action Steps
- Evaluate the concept-based explainability of frozen MLLMs under few-shot ICL using multiple conditions
- Apply Chain-of-Thought prompting to MLLMs and assess its effectiveness in reflecting true internal computation
- Compare the performance of MLLMs with different explanation methods
- Analyze the results to identify the challenges of explaining MLLM predictions
- Test the robustness of concept-based explanations in various ICL scenarios
Who Needs to Know This
Researchers and developers working with multimodal large language models (MLLMs) and in-context learning (ICL) can benefit from understanding the limitations of concept-based explanations
Key Insight
💡 Concept-based explanations of MLLMs are limited and may not reflect true internal computation
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🤖 Explaining MLLM predictions is harder than predicting alone! 📊 New research evaluates concept-based explanations in ICL visual classification
Key Takeaways
Evaluating concept-based explanations of MLLMs as ICL visual classifiers reveals that explaining is harder than predicting alone
Full Article
Title: Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers
Abstract:
arXiv:2605.28215v1 Announce Type: new Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples. Yet, how these models use the provided context remains opaque. While Chain-of-Thought prompting is widely used, recent work argues that it may not reflect true internal computation. In this paper, we systematically evaluate the concept-based explainability of frozen MLLMs under few-shot ICL using five conditions of increasing
Abstract:
arXiv:2605.28215v1 Announce Type: new Abstract: In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples. Yet, how these models use the provided context remains opaque. While Chain-of-Thought prompting is widely used, recent work argues that it may not reflect true internal computation. In this paper, we systematically evaluate the concept-based explainability of frozen MLLMs under few-shot ICL using five conditions of increasing
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