Logical Reasoning

Logical reasoning in artificial intelligence focuses on developing models capable of performing complex deductive and inductive inferences, mirroring human-like reasoning abilities. Current research emphasizes improving large language models (LLMs) through techniques like chain-of-thought prompting, process supervision during pre-training, and integration with symbolic reasoning systems such as automated theorem provers. These advancements aim to address LLMs' tendency to rely on superficial patterns rather than true logical understanding, ultimately improving the reliability and trustworthiness of AI systems in various applications, including scientific discovery and legal reasoning. The development of robust benchmarks, such as those based on 3-SAT problems and various logic games, is crucial for evaluating and driving progress in this field.