Symbolic Approach

The symbolic approach in artificial intelligence aims to integrate logical reasoning and knowledge representation with the power of machine learning, addressing limitations of purely data-driven methods in tasks requiring complex reasoning and explainability. Current research focuses on hybrid models combining large language models (LLMs) with symbolic executors or formal methods tools, leveraging LLMs for tasks like specification synthesis and using symbolic methods for verification, refinement, and explanation generation. This neuro-symbolic integration is proving valuable for improving the performance and interpretability of AI systems across diverse applications, including natural language processing, program verification, and explainable AI.