Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (2024)

Abstract: Reasoning, a fundamental cognitive process integral to human intelligence, has garnered substantial interest within artificial intelligence.Notably, recent studies have revealed that chain-of-thought prompting significantly enhances LLM’s reasoning capabilities, which attracts widespread attention from both academics and industry.In this paper, we systematically investigate relevant research, summarizing advanced methods through a meticulous taxonomy that offers novel perspectives.Moreover, we delve into the current frontiers and delineate the challenges and future directions, thereby shedding light on future research.Furthermore, we engage in a discussion about open questions.We hope this paper serves as an introduction for beginners and fosters future research.Resources have been made publicly available at https://github.com/zchuz/CoT-Reasoning-Survey

## introduction in the realm of human cognition, reasoning stands as the linchpin, essential in the understanding of the world and the formation of our decisions. as the scale of pre-training continues to expand @xcite @xcite , large language models (llms) exhibit growing capabilities in numerous downstream tasks @xcite @xcite . recently, researchers have discovered that llms emerge with the capability for step-by-step reasoning through in-context learning, a phenomenon referred to as chain-of-thought (cot) reasoning. it is broadly observed that cot prompting significantly boosts the reasoning abilities of llms, especially in complex tasks @xcite @xcite . ## benchmarks in this section, we briefly outline the benchmarks for evaluating reasoning capabilities, including mathematical, commonsense, symbolic, logical, and multi-modal reasoning. the overview of benchmarks is shown in mathematical reasoning mathematical reasoning forms the foundation of human intelligence, playing a crucial role in problem-solving, decisionmaking, and world comprehension. it is commonly used to assess the general reasoning ability of llms @xcite @xcite . commonsense reasoning commonsense reasoning is essential for the interaction in daily life and the perception of the world, which assesses the world comprehension capacity of language models @xcite @xcite . ## advanced methods this section discusses advanced xot methods from three viewpoints: prompt construction ( §4.1), topological variations ( §4.2), and enhancement methods ( §4.3). the taxonomy is shown in figure 2 . ## future directions despite xot reasoning has showcased remarkable performance on numerous tasks, there are still some challenges that necessitate further research. ## discussion we delve into open questions about chain-ofthought reasoning, with the details discussion in appendix a.2. the discussion encompasses three topics: (a) how does chain-of-thought reasoning ability emerge with large-scale pre-training? (b) how to provide accurate feedback for a model's reasoning and decision-making. (c) the implications of chain-of-thought reasoning for llm-powered autonomous agents and agi.