Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering
arXiv:2606.03269v1 Announce Type: new Abstract: Visual Question Answering (VQA) is the task of answering questions about images, requiring the integration of multimodal input and reasoning. Modular approaches that incorporate logic-based representations into the reasoning component offer clear advantages over end-to-end trained systems, particularly in terms of interpretability. However, adapting or extending these representations when task requirements change can place a significant burden on developers. To address this challenge, we present an approach for distilling rules from Large Languag
The proliferation of powerful LLMs is prompting research into more interpretable and adaptable AI systems, particularly for complex tasks like VQA.
This development addresses a critical limitation of current end-to-end AI models by enhancing interpretability and making AI systems easier to adapt and extend, which is vital for trust and practical application.
The ability to distill explicit rules from LLMs introduces a new method for neurosymbolic AI, combining the power of neural networks with the explainability of symbolic logic.
- · AI researchers
- · Developers of VQA systems
- · Industries requiring explainable AI
- · Purely black-box end-to-end AI systems without interpretability
Improved interpretability and adaptability of AI models in complex domains like VQA.
Faster development and deployment of domain-specific AI applications due to easier rule modification and integration.
Increased adoption of neurosymbolic AI architectures across various AI tasks, potentially standardizing hybrid approaches for complex reasoning.
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Read at arXiv cs.AI