Towards Reasoning in Large Language Models: A Survey
It addresses the need for clarity on reasoning abilities in LLMs, which is crucial for researchers and practitioners in AI and NLP, but is incremental as it synthesizes existing knowledge rather than introducing novel methods.
This survey paper tackles the problem of understanding and enhancing reasoning capabilities in large language models (LLMs), providing a comprehensive overview of current techniques, evaluations, and research findings without presenting new experimental results.
Reasoning is a fundamental aspect of human intelligence that plays a crucial role in activities such as problem solving, decision making, and critical thinking. In recent years, large language models (LLMs) have made significant progress in natural language processing, and there is observation that these models may exhibit reasoning abilities when they are sufficiently large. However, it is not yet clear to what extent LLMs are capable of reasoning. This paper provides a comprehensive overview of the current state of knowledge on reasoning in LLMs, including techniques for improving and eliciting reasoning in these models, methods and benchmarks for evaluating reasoning abilities, findings and implications of previous research in this field, and suggestions on future directions. Our aim is to provide a detailed and up-to-date review of this topic and stimulate meaningful discussion and future work.