Deduction System

Deduction systems aim to improve the reasoning capabilities of artificial intelligence, particularly large language models (LLMs), which currently rely heavily on inductive methods. Research focuses on integrating deductive reasoning into existing LLMs through techniques like Bayesian inference frameworks, incorporating expert rules and logical constraints, and leveraging the models' own reasoning abilities during training (e.g., deductive closure training). These advancements enhance the reliability, coherence, and trustworthiness of AI outputs, with applications ranging from improved fact verification and knowledge base completion to more robust decision-making in real-world scenarios.