Reasoning Task

Reasoning tasks in large language models (LLMs) focus on improving the ability of these models to perform multi-step inferences and solve complex problems requiring logical deduction and induction. Current research emphasizes developing novel prompting techniques, such as those inspired by Bloom's taxonomy or employing dynamic reasoning trajectories, and improving model training through knowledge distillation and learning from mistakes. These advancements are significant because enhanced reasoning capabilities in LLMs have broad implications for various fields, including improving question answering systems, enhancing personalized recommendation systems, and advancing applications in education and scientific discovery.