Symbolic Language

Symbolic language research focuses on enabling artificial intelligence systems to effectively process and reason with symbolic representations, such as mathematical formulas or code, rather than solely relying on natural language. Current research emphasizes bridging the gap between large language models (LLMs) and symbolic reasoning, often by developing methods to translate symbols into natural language for LLM processing or by training smaller, more efficient models on LLM-generated symbolic data. This work is significant because it addresses the limitations of purely neural approaches, improving the interpretability, robustness, and efficiency of AI systems across diverse applications like abstract reasoning, code generation, and scientific discovery.