NeuroProlog: Multi-Task Fine-Tuning for Neurosymbolic Mathematical Reasoning via the Cocktail Effect

Researchers introduce NeuroProlog, a neurosymbolic framework that improves mathematical reasoning in Large Language Models by converting math problems into executable Prolog programs. The multi-task 'Cocktail' training approach shows significant accuracy improvements of 3-5% across different model sizes, with larger models demonstrating better error correction capabilities.