2024 ToCoTOrNottoCoTChainofThoughtHe
- (Sprague et al., 2024) ⇒ Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, Dongwei Jiang, Manya Wadhwa, Prasann Singhal, Xinyu Zhao, Xi Ye, Kyle Mahowald, and Greg Durrett. (2024). “To CoT Or Not to CoT? Chain-of-thought Helps Mainly on Math and Symbolic Reasoning.” doi:10.48550/arXiv.2409.12183
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Notes
- The paper presents a meta-analysis covering over 100 research papers to systematically evaluate the effectiveness of Chain-of-Thought (CoT) prompting across various large language models and reasoning tasks.
- The paper finds that CoT is most beneficial for tasks involving mathematical, logical, and symbolic reasoning, such as complex multi-step calculations or algorithmic problem-solving, providing strong performance gains in these specific domains.
- The paper reveals that for commonsense reasoning and natural language understanding, CoT prompts often show negligible or negative improvements compared to direct-answer prompts, suggesting CoT is not universally applicable.
- The paper highlights that, on the MMLU (Massive Multitask Language Understanding dataset), 95% of CoT’s gains are attributed to questions containing an “equals sign”, demonstrating that CoT’s benefits are confined to tasks involving explicit symbolic operations.
- The paper argues that CoT primarily enhances the execution phase of problem-solving, where tracking intermediate steps is essential, while its impact on the initial planning and problem formulation stage remains limited.
- The paper suggests that tool-augmented approaches, such as using Python interpreters or Satisfiability Modulo Theory (SMT) solvers, outperform CoT for symbolic reasoning tasks, highlighting CoT’s role as a less efficient substitute for tool-based reasoning.
- The paper recommends selective application of CoT only for tasks with clearly defined symbolic structures, advocating for a more cost-effective approach that avoids unnecessary computational overhead.
- The paper connects to related work by Nye et al. (2022) and Wei et al. (2022) on Scratchpads and step-by-step reasoning, demonstrating that CoT’s step-by-step approach is not universally beneficial and must be evaluated in context-specific scenarios.
- The paper challenges the conventional assumption that CoT universally improves reasoning, arguing that CoT’s effectiveness is task-specific and limited to symbolic and algorithmic problems, thereby questioning its general applicability.
- The paper concludes by calling for new paradigms beyond prompt-based CoT, such as interactive models, agent-based reasoning, or more sophisticated intermediate computation techniques, to expand CoT’s utility to a broader set of reasoning challenges.
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Abstract
Chain-of-thought (CoT) via prompting is the de facto method for eliciting reasoning capabilities from large language models (LLMs). But for what kinds of tasks is [[this extra ``thinkin really helpful]]? To analyze this, we conducted a quantitative meta-analysis covering over 100 papers using CoT and ran our own evaluations of 20 datasets across 14 models. Our results show that CoT gives strong performance benefits primarily on tasks involving math or logic, with much smaller gains on other types of tasks. On MMLU, directly generating the answer without CoT leads to almost identical accuracy as CoT unless the question or model's response contains an equals sign, indicating symbolic operations and reasoning. Following this finding, we analyze the behavior of CoT on these problems by separating planning and execution and comparing against tool-augmented LLMs. Much of CoT's gain comes from improving symbolic execution, but it underperforms relative to using a symbolic solver. Our results indicate that CoT can be applied selectively, maintaining performance while saving inference costs. Furthermore, they suggest a need to move beyond prompt-based CoT to new paradigms that better leverage intermediate computation across the whole range of LLM applications.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024 ToCoTOrNottoCoTChainofThoughtHe | Greg Durrett Zayne Sprague Fangcong Yin Juan Diego Rodriguez Dongwei Jiang Manya Wadhwa Prasann Singhal Xinyu Zhao Xi Ye Kyle Mahowald | To CoT Or Not to CoT? Chain-of-thought Helps Mainly on Math and Symbolic Reasoning | 10.48550/arXiv.2409.12183 | 2024 |