LLMs Do Not Think Step-by-step In Implicit Reasoning
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.
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Yijiong Yu
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2024-11-26T21:20:42.602Z
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yuyijiong
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yuyijiong
LLMs Do Not Think Step-by-step In Implicit Reasoning
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.
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.
Chain-of-Thought LLMs inference speeds computational costs implicit CoT explicit CoT hidden states step-by-step reasoning