This paper investigates the internal reasoning mechanisms of large language models (LLMs) during symbolic multi-step reasoning tasks, particularly focusing on whether the models arrive at answers before or during the generation of their chain-of-thought (CoT) explanations. The central question is whether LLMs operate in a “think-to-talk” mode, where they determine the answer internally before producing an explanation, or a “talk-to-think” mode, where the reasoning unfolds step-by-step as the explanation is generated.
Methodology
To explore this, the authors designed controlled arithmetic reasoning tasks of varying complexities, structured into five levels. Each task involves computing the values of variables through a series of arithmetic operations, with some tasks requiring multi-step reasoning and others including distractor equations irrelevant to the final answer.
They used linear probing techniques. These techniques analyzed the hidden states of ten different LLMs at each layer and timestep during the task processing. Probing classifiers were trained to predict the values of intermediate variables based on the models’ internal representations. By examining when these probes could accurately predict variable values, the researchers aimed to determine at which point in the input-output sequence the models internally compute different parts of the problem.
Findings
The analysis revealed systematic internal reasoning patterns across the models studied:
- Early Computation of Simple Subproblems: For tasks involving simple, single-step reasoning (e.g., computing A=2+3A = 2 + 3A=2+3), the models often computed the answers before the CoT began. Probes could accurately predict the values of such variables from the hidden states associated with the input portion of the sequence.
- Gradual Computation During CoT for Complex Problems: In tasks requiring more complex, multi-step reasoning (e.g., where one variable depends on another computed variable), the models tended to compute intermediate values during the generation of the CoT. The probes’ accuracy in predicting these variables increased at timesteps corresponding to the relevant steps in the CoT.
- Impact of Predetermined Sub-Answers: Through causal interventions—specifically, activation patching where certain hidden states were replaced—the study found that predetermined sub-answers influenced the final answer, though the relationship was not always direct. Altering hidden states associated with high probe accuracy for a variable could change the final output, confirming a causal link.
- Indirect Causality in Conflicting Information: When the models were provided with conflicting information (e.g., changing inputs while forcing the original CoT), the final answers aligned with the CoT rather than the modified inputs. This suggests that while internal prior computations influence the final answer, the models can override them based on new contextual information.
Implications
These findings suggest that LLMs exhibit both think-to-talk and talk-to-think reasoning modes:
- Think-to-Talk: For simpler problems, models seem to compute answers internally before generating explanations, indicating a post-hoc explanation process.
- Talk-to-Think: For more complex problems, the reasoning unfolds alongside the generation of the CoT, reflecting a step-by-step problem-solving process.
Understanding these internal reasoning patterns is crucial for interpreting LLM behavior. It improves their transparency. It can also potentially guide the development of models that can reason more like humans.
Limitations
The study acknowledges certain limitations:
- Synthetic Nature of Tasks: The use of controlled arithmetic tasks allows for precise analysis but may not capture the full complexity of natural language reasoning tasks. Further research is needed to generalize these findings to more diverse and realistic scenarios.
- Probing Methodology: While linear probing is useful for interpreting model internals, it may not reveal all aspects of the reasoning process. There is also ongoing debate about the validity and limitations of probing methods in understanding deep learning models.
Conclusion
The paper contributes to the mechanistic understanding of how LLMs process multi-step reasoning tasks. It reveals when models compute answers internally. It also shows how these computations relate to their generated explanations. Together, these insights provide a deeper understanding of their reasoning dynamics. This knowledge is valuable for developing more transparent AI systems and for refining techniques to steer their reasoning processes effectively.
