Decoding Answers Before Chain-of-Thought: Evidence from Pre-CoT Probes and Activation Steering
Kyle Cox, Darius Kianersi, Adri\`a Garriga-Alonso

TL;DR
This paper shows that large language models often decide their answers before generating chain-of-thought explanations, and that their final answers can be predicted and manipulated by steering model activations, raising interpretability and faithfulness concerns.
Contribution
It provides mechanistic evidence that models determine answers pre-CoT and demonstrates causal steering of model answers through activation manipulation.
Findings
Model answers can be predicted with high accuracy before CoT generation.
Activation steering can flip model answers in over 50% of cases.
Pre-CoT beliefs influence the correctness and reasoning modes of the model.
Abstract
As chain-of-thought (CoT) has become central to scaling reasoning capabilities in large language models (LLMs), it has also emerged as a promising tool for interpretability, suggesting the opportunity to understand model decisions through verbalized reasoning. However, the utility of CoT toward interpretability depends upon its faithfulness -- whether the model's stated reasoning reflects the underlying decision process. We provide mechanistic evidence that instruction-tuned models often determine their answer before generating CoT. Training linear probes on residual stream activations at the last token before CoT, we can predict the model's final answer with 0.9 AUC on most tasks. We find that these directions are not only predictive, but also causal: steering activations along the probe direction flips model answers in over 50% of cases, significantly exceeding orthogonal baselines.…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Multimodal Machine Learning Applications · Topic Modeling
