Reasoning in Large Language Models: Methods, Challenges, and Future Directions
TL;DR
This paper reviews several methods aimed at improving reasoning in large language models, including prompt-based approaches, including Chain-of-Thought, self-consistency, and Auto- CoT, which try to guide models to generate intermediate reasoning steps.
Abstract
In recent years, large language models have developed rapidly and are now widely used in many everyday tasks, such as conversation, information retrieval, and code generation. Although these models can produce fluent and coherent text, their ability to perform reliable reasoning is still limited, especially in tasks that require multiple steps or logical consistency. This paper reviews several methods aimed at improving reasoning in LLMs. It first introduces prompt-based approaches, including Chain-of-Thought, self-consistency, and Auto- CoT, which try to guide models to generate intermediate reasoning steps. It then discusses search-based methods, such as Tree-of-Thought, in which reasoning is treated as a process of exploring multiple paths. In addition, the paper examines the hallucination problem and the use of retrieval-based techniques to improve factual accuracy. Benchmark datasets for evaluating reasoning ability are also briefly discussed. Furthermore, the paper examines recent developments in agent-based reasoning and tool use, in wh ich models can interact with external systems and perform more complex tasks. While these methods show some improvements, several open challenges remain, including issues of reasoning reliability, error accumulation, and evaluation.