Reasoning Strategy

Reasoning strategy research focuses on enhancing the logical capabilities of large language models (LLMs) to solve complex problems, moving beyond simple pattern recognition. Current efforts concentrate on developing and evaluating diverse reasoning approaches, including deductive, inductive, abductive, and analogical reasoning, often implemented through techniques like chain-of-thought prompting, tree-of-thoughts, and agent-based methods. These advancements aim to improve LLMs' accuracy, efficiency, and explainability across various tasks, impacting fields like question answering, planning, and decision-making. A key challenge is balancing the performance gains of complex strategies against their computational cost, with a growing emphasis on efficient and robust methods.