Overview Papers on Reasoning in LLMs

Several research papers provide a comprehensive survey of cutting-edge research on reasoning with large language models. Here are a few that might worth your attention:

• Reasoning with Language Model Prompting: A Survey. This paper, first published in December 2022, may not cover the most recent developments in LLM reasoning but still offers a comprehensive survey of available approaches. It identifies and details various methods, organizing them into categories such as strategic enhancements and knowledge enhancements. The authors describe multiple reasoning strategies, including chain-of-thought prompting and more sophisticated techniques that combine human-like reasoning processes with external computation engines to enhance performance.
• Towards Reasoning in Large Language Models: A Survey. This paper, also from December 2022, provides a comprehensive survey of reasoning in LLMs, discussing the current understanding, challenges, and methodologies for eliciting reasoning from LLMs, as well as evaluating their reasoning capabilities. The authors present a detailed analysis of various approaches to enhance reasoning, the development of benchmarks to measure reasoning abilities, and a discussion on the implications of these findings. They also explore the potential future directions in the field, aiming to bridge the gap between LLM capabilities and human-like reasoning.
• Large Language Models Cannot Self-Correct Reasoning Yet. In this more recent research paper from October 2023, the researchers from the Google DeepMind team critically examine the capability of LLMs to perform intrinsic self-correction, a process where an LLM corrects its initial responses without external feedback. They find that LLMs generally struggle to self-correct their reasoning, often performing worse after attempting to self-correct. This paper, to be soon presented at ICLR 2024, provides a detailed analysis of self-correction methods, demonstrating through various tests that improvements seen in previous studies typically rely on external feedback mechanisms, such as oracle labels, which are not always available or practical in real-world applications. The findings prompt a reevaluation of the practical applications of self-correction in LLMs and suggest directions for future research to address these challenges.

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