arXiv:2606.19735v1 Announce Type: new Abstract: While global explanations are crucial for understanding vision models across datasets, classes, and decision contexts, their complex and monolithic nature often hinders practical exploration. Because users typically seek targeted answers to specific questions rather than static artifacts, we present an LLM-based interactive interface that provides natural language access to global explanations for black-box image classifiers. The system's core LLM acts as a mediator, translating natural language questions into structured SQL queries over local ex
The proliferation of complex AI models creates an urgent need for intuitive explanation interfaces, and advances in LLMs now enable their use for translation into structured queries.
This development moves towards making sophisticated AI models more transparent and interpretable for non-technical users, broadening their adoption and enabling better decision-making.
The ability to query complex global explanations for black-box AI models using natural language fundamentally changes how users interact with and understand AI systems.
- · AI developers
- · Businesses adopting AI
- · AI ethicists and regulators
- · Data scientists
- · Companies with opaque AI systems
- · Traditional static explanation methods
More widespread and effective adoption of complex AI in critical domains due to increased interpretability.
Improved trust and reduced regulatory friction for AI systems as their decision-making becomes more transparent.
The development of an 'explanation-as-a-service' industry around AI models, leveraging natural language interfaces.
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Read at arXiv cs.AI