Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering
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 Language Models (LLMs). Our method prompts an LLM to extend an initial VQA reasoning theory, expressed as an answer-set program, to meet new requirement...
| Authors | Thomas Eiter, Nelson Higuera Ruiz, Johannes Oetsch |
|---|---|
| Primary category | cs.AI |
| Published | 2 June 2026 |
| Last updated at source | 26 June 2026 |
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