Reasoning Ability

Reasoning ability in large language models (LLMs) is a burgeoning research area focused on evaluating and enhancing the capacity of these models to perform multi-step inferences and solve complex problems requiring logical deduction and inductive learning. Current research emphasizes benchmarking LLMs on diverse tasks, including mathematical reasoning, commonsense reasoning, and following procedures, often employing techniques like chain-of-thought prompting and knowledge distillation to improve performance. Understanding and improving LLM reasoning is crucial for building more reliable and trustworthy AI systems with broader applications across various fields, from scientific discovery to decision-making support.