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Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (2309.15402v3)

Published 27 Sep 2023 in cs.CL and cs.AI
Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future

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

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A Comprehensive Survey of Chain-of-Thought Reasoning

The paper "A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future" serves as a detailed examination of the development and application of chain-of-thought (CoT) reasoning in the context of artificial intelligence and natural language processing. Chain-of-thought reasoning methods leverage the cognitive processes foundational to human intelligence for enhancing the reasoning capabilities of pre-trained LLMs (PLMs) and LLMs. This survey is notable for its extensive coverage of various methodologies, applications, and potential future directions in this rapidly developing field.

The core of this survey categorizes chain-of-thought reasoning methods into three main approaches: manual, automatic, and semi-automatic. Manual CoT methods involve explicitly annotating demonstrations with thought processes, thus allowing LLMs to mimic complex reasoning steps. Automatic CoT eliminates human intervention by utilizing zero-shot prompt engineering or sampling to generate reasoning paths, albeit with the risk of diminished quality due to the lack of human alignment. The intermediate semi-automatic approach blends elements of both manual and automatic CoT to balance cost, quality, and task generalization.

A significant portion of the survey is dedicated to structural variants of chain-of-thought reasoning. These include adaptations leading to distinct configurations, such as chain, tree, and graph structures. Tree-of-Thought (ToT) and Graph-of-Thought (GoT) illustrate how reasoning can parallel human methods of exploration and backtracking to resemble decisions made in complex scenarios. While these structures enhance the flexibility and capability of reasoning models, they present increased complexity, which restricts their general applicability across diverse tasks.

Additionally, the survey discusses techniques to enhance CoT reasoning such as verification and refinement, which reduces errors through feedback mechanisms and iterative processes. Question decomposition provides a structured approach to tackling complex problems by breaking them down into manageable segments. The incorporation of external knowledge resources mitigates limitations inherent to LLMs in terms of up-to-date information access and factual accuracy. Elements such as voting, ranking, and efficiency techniques further refine the robustness of existing CoT frameworks by enhancing decision-making consistency and reducing computational overhead.

Importantly, the paper situates CoT reasoning within broader applications, highlighting its potential in areas such as tool use, by enriching models with external resources and interfaces to boost reasoning power and access additional data. Planning exemplifies methodical decomposition of tasks into achievable steps, improving problem-solving for intricate tasks. CoT distillation is explored as a means to transfer reasoning capacities from LLMs to smaller, more scalable models, thereby increasing access to advanced capabilities.

To chart future progress, the paper emphasizes enhancing CoT reasoning in multi-modal contexts, increasing faithfulness by reducing hallucinatory outputs, and theoretically grounding CoT methods. The integration of intermediate structures that can handle multi-modal data is poised to significantly expand the contextual reasoning capabilities of AI models. Additionally, addressing faithfulness remains crucial, aiming to improve the factual correctness of AI-generated CoT outputs. The understanding of CoT from both an empirical and theoretical perspective is an area called for further exploration, offering promise for refining reasoning frameworks.

In summary, this survey not only serves as a comprehensive guide to current advancements in chain-of-thought reasoning but also encourages future research to overcome existing limitations and expand the application scope of CoT-enabled PLMs and LLMs. As AI systems increasingly adopt reasoning models, the insights offered in this paper provide a solid foundation for advancing intelligent systems capable of human-like reasoning.

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Authors (10)
  1. Zheng Chu (49 papers)
  2. Jingchang Chen (10 papers)
  3. Qianglong Chen (25 papers)
  4. Weijiang Yu (23 papers)
  5. Tao He (62 papers)
  6. Haotian Wang (60 papers)
  7. Weihua Peng (12 papers)
  8. Ming Liu (421 papers)
  9. Bing Qin (186 papers)
  10. Ting Liu (329 papers)
Citations (97)
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GitHub

  1. GitHub - zchuz/CoT-Reasoning-Survey: A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future (230 stars)