Knowing Before Saying: LLM Representations Encode Information About
  Chain-of-Thought Success Before Completion
We investigate whether the success of a zero-shot Chain-of-Thought (CoT)
process can be predicted before completion. We discover that a probing
classifier, based on LLM representations, performs well even before a
single token is generated, suggesting that crucial information about the
reasoning process is already present in the initial steps representations. In
contrast, a strong BERT-based baseline, which relies solely on the generated
tokens, performs worse, likely because it depends on shallow linguistic cues
rather than deeper reasoning dynamics. Surprisingly, using later reasoning
steps does not always improve classification. When additional context is
unhelpful, earlier representations resemble later ones more, suggesting LLMs
encode key information early. This implies reasoning can often stop early
without loss. To test this, we conduct early stopping experiments, showing that
truncating CoT reasoning still improves performance over not using CoT at all,
though a gap remains compared to full reasoning. However, approaches like
supervised learning or reinforcement learning designed to shorten CoT chains
could leverage our classifier's guidance to identify when early stopping is
effective. Our findings provide insights that may support such methods, helping
to optimize CoT's efficiency while preserving its benefits.
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Anum Afzal
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Anum Afzal
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Florian Matthes
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Gal Chechik
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Gal Chechik
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Yftah Ziser
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2025-06-04T09:22:13.900Z
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Yftah Ziser
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yziser
Knowing Before Saying: LLM Representations Encode Information About
  Chain-of-Thought Success Before Completion
We investigate whether the success of a zero-shot Chain-of-Thought (CoT)
process can be predicted before completion. We discover that a probing
classifier, based on LLM representations, performs well even before a
single token is generated, suggesting that crucial information about the
reasoning process is already present in the initial steps representations. In
contrast, a strong BERT-based baseline, which relies solely on the generated
tokens, performs worse, likely because it depends on shallow linguistic cues
rather than deeper reasoning dynamics. Surprisingly, using later reasoning
steps does not always improve classification. When additional context is
unhelpful, earlier representations resemble later ones more, suggesting LLMs
encode key information early. This implies reasoning can often stop early
without loss. To test this, we conduct early stopping experiments, showing that
truncating CoT reasoning still improves performance over not using CoT at all,
though a gap remains compared to full reasoning. However, approaches like
supervised learning or reinforcement learning designed to shorten CoT chains
could leverage our classifier's guidance to identify when early stopping is
effective. Our findings provide insights that may support such methods, helping
to optimize CoT's efficiency while preserving its benefits.
A classifier based on LLM representations predicts successful zero-shot CoT processes before generating tokens, suggesting that key reasoning information is captured early, and early stopping can improve performance.
zero-shot Chain-of-Thought LLM representations BERT-based baseline shallow linguistic cues deeper reasoning dynamics early stopping experiments supervised learning reinforcement learning