title: LLMs Do Not Think Step-by-step In Implicit Reasoning
summary: It has been well-known that Chain-of-Thought can remarkably enhance LLMs'
  performance on complex tasks. However, because it also introduces slower
  inference speeds and higher computational costs, many researches have attempted
  to use implicit CoT, which does not need LLMs to explicitly generate the
  intermediate steps. But there is still gap between their efficacy and typical
  explicit CoT methods. This leaves us a doubt that, does implicit CoT really
  equal to explicit CoT? Therefore, in this study, we address this question
  through experiments. We probe the information of intermediate steps from the
  model's hidden states when it is performing implicit CoT. The results
  surprisingly indicate that LLMs hardly think about intermediate steps,
  suggesting they may just rely on experience rather than strict step-by-step
  reasoning. Moreover, we find LLMs' implicit reasoning capabilities are
  susceptible and unstable, reaffirming the necessity of explicit CoT to
  effectively support complex tasks.
paper_authors._id: 6745c2f874d673071da3303f
paper_authors.name: Yijiong Yu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-26T21:20:42.602Z
paper_authors._id: 6374c494958cd71fa7ea0a9d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6374c494958cd71fa7ea0a9d/2YCKv6tXCZXtsIOFIIXjs.png
paper_authors.fullname: yuyijiong
paper_authors.type: user
paper_authors.user: yuyijiong
paper_title: LLMs Do Not Think Step-by-step In Implicit Reasoning
paper_summary: It has been well-known that Chain-of-Thought can remarkably enhance LLMs'
  performance on complex tasks. However, because it also introduces slower
  inference speeds and higher computational costs, many researches have attempted
  to use implicit CoT, which does not need LLMs to explicitly generate the
  intermediate steps. But there is still gap between their efficacy and typical
  explicit CoT methods. This leaves us a doubt that, does implicit CoT really
  equal to explicit CoT? Therefore, in this study, we address this question
  through experiments. We probe the information of intermediate steps from the
  model's hidden states when it is performing implicit CoT. The results
  surprisingly indicate that LLMs hardly think about intermediate steps,
  suggesting they may just rely on experience rather than strict step-by-step
  reasoning. Moreover, we find LLMs' implicit reasoning capabilities are
  susceptible and unstable, reaffirming the necessity of explicit CoT to
  effectively support complex tasks.
paper_ai_summary: Experiments exploring implicit Chain-of-Thought in LLMs suggest that models do not engage in step-by-step reasoning and rely more on experience, highlighting the continued importance of explicit CoT for complex tasks.
paper_ai_keywords: Chain-of-Thought LLMs inference speeds computational costs implicit CoT explicit CoT hidden states step-by-step reasoning