Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering

2026-06-02 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

An approach distills Answer-Set Programming (ASP) rules from Large Language Models (LLMs) to enhance Neurosymbolic Visual Question Answering (VQA). This method addresses the challenge of adapting logic-based representations in VQA by enabling LLMs to extend an initial VQA reasoning theory, expressed as an answer-set program, for new task requirements. The process leverages examples from VQA datasets to guide the LLM, validate the generated rules, and correct errors using feedback from an ASP solver. This technique demonstrates effectiveness across diverse VQA datasets, notably requiring only a few examples to elicit accurate rules from LLMs. It offers a promising alternative to traditional data-driven rule learning approaches for VQA.

Key takeaway

For AI Scientists developing neurosymbolic VQA systems, this rule distillation method offers a significant efficiency gain. If you are struggling with the burden of manually adapting logic-based representations for new task requirements, consider leveraging LLMs to extend your answer-set programs. This approach requires only a few examples, streamlining the development and maintenance of interpretable VQA models and potentially accelerating your research into robust reasoning systems.

Key insights

Distilling ASP rules from LLMs simplifies adapting logic-based VQA reasoning theories with minimal examples.

Principles

• LLMs can extend logic-based reasoning theories.
• Feedback from solvers corrects LLM-generated rules.
• Few examples suffice for LLM rule elicitation.

Method

Prompt an LLM to extend an initial VQA reasoning theory (answer-set program) using VQA examples for guidance, validation, and error correction via ASP solver feedback.

In practice

• Use LLMs to adapt VQA logic representations.
• Integrate ASP solvers for rule validation.
• Reduce data needs for rule learning.

Topics

• Visual Question Answering
• Large Language Models
• Answer-Set Programming
• Neurosymbolic AI
• Rule Distillation
• Logic-based Reasoning

Best for: AI Scientist, Research Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.