Reasoning Capability

Reasoning capability in large language models (LLMs) is a central research area focusing on enhancing their ability to solve complex problems requiring multiple steps and logical inferences. Current research investigates various prompting techniques, such as chain-of-thought prompting and retrieval-augmented generation (RAG), to improve reasoning performance across diverse tasks, including mathematical, logical, and commonsense reasoning, often using benchmarks like GSM8K and its variants. These efforts aim to understand the limitations of current LLMs, which often rely on pattern matching rather than true logical deduction, and to develop more robust and reliable reasoning methods. The ultimate goal is to create LLMs capable of genuine reasoning, impacting fields ranging from scientific discovery to personalized education and decision support systems.