The exploration of reasoning in Large Language Models has gained attention, particularly as these models are increasingly relied upon for generating coherent text.
While the ability of LLMs to produce fluent prose is well-established through machine learning principles, there is a noted lack of a similarly robust framework to support their reasoning processes.
This gap raises questions about the reliability of the conclusions drawn by these models, emphasizing the need for further research to enhance their reasoning abilities.