Low-cost concept-based localized explanations: How far can we get with training-free approaches?
arXiv:2606.29069v1 Announce Type: new Abstract: Concept-based Explainable AI (C-XAI) seeks human-understandable explanations grounded in semantic concepts, yet validation is limited by the scarcity of fine-grained concept annotations. We evaluate whether mid-scale Multimodal Large Language Models (MLLMs) can perform localized concept naming under strict zero-shot conditions by assigning labels to bounding-box regions at both object and part levels. We propose a reproducible zero-shot evaluation protocol for Concept Naming (CoNa) with (i) closed-set, category-constrained prompting for moderate
This development is emerging now due to the rapid advancements in Multimodal Large Language Models (MLLMs) and the increasing demand for transparent and interpretable AI systems.
A strategic reader should care because improving the explainability of AI, especially through low-cost, training-free methods, accelerates adoption and trust in complex AI applications across various industries.
The ability to generate localized, concept-based explanations without extensive fine-grained annotations dramatically lowers the barrier to entry for explainable AI, making it more widely accessible.
- · AI developers
- · AI adopters
- · MLOps platforms
- · AI ethics researchers
- · Proprietary XAI solutions with high annotation costs
- · AI systems lacking transparency
Easier debugging and validation of complex MLLMs leading to more robust and reliable AI deployments.
Increased legal and regulatory confidence in AI systems as their decision-making processes become more understandable.
Acceleration of AI integration into sensitive domains like healthcare and finance due to enhanced interpretability and accountability.
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Read at arXiv cs.AI