Unveiling and Causalizing CoT: A Causal Pespective (2502.18239v1)

Abstract: Although Chain-of-Thought (CoT) has achieved remarkable success in enhancing the reasoning ability of LLMs, the mechanism of CoT remains a ``black box''. Even if the correct answers can frequently be obtained, existing CoTs struggle to make the reasoning understandable to human. In this paper, we unveil and causalize CoT from a causal perspective to ensure both correctness and understandability of all reasoning steps (to the best of our knowledge, the first such). We model causality of CoT via structural causal models (SCM) to unveil the reasoning mechanism of CoT. To measure the causality of CoT, we define the CoT Average Causal Effect (CACE) to test the causal relations between steps. For those steps without causality (wrong or unintelligible steps), we design a role-playing causal query algorithm to causalize these steps, resulting a causalized CoT with all steps correct and understandable. Experimental results on both open-source and closed-source LLMs demonstrate that the causal errors commonly in steps are effectively corrected and the reasoning ability of LLMs is significantly improved.

Unveiling and Causalizing Chain-of-Thought: A Causal Perspective

The paper "Unveiling and Causalizing CoT: A Causal Perspective" investigates the reasoning mechanisms within LLMs via Chain-of-Thought (CoT) from a causal standpoint. Although CoT has significantly improved the reasoning capabilities of LLMs, it remains a largely opaque process. This paper seeks to elucidate the causality behind CoT and ensure its reasoning steps are both correct and comprehensible by modeling it through Structural Causal Models (SCMs).

Main Contributions

The research presents several noteworthy contributions:

1. Causal Modeling of CoT: The authors introduce SCMs to model the causal relationships inherent in CoT. This approach makes the reasoning processes of LLMs interpretable by theorizing that CoT's effectiveness arises from its reflection of real-world causal relationships.
2. Quantifying Causality in CoT: The paper defines CoT Average Causal Effect (CACE) and First-Step Causal Effect (FSCE) to measure causal relationships within CoT by assessing both the logical and answer-based aspects of reasoning. These quantifications enable the testing of causal relations between steps in a CoT sequence.
3. Causalizing Algorithm: A novel role-playing causal query algorithm is proposed to correct steps in CoT lacking causal logic. This includes a two-step process of role-playing queries and refinement, thus ensuring the correctness and comprehensibility of reasoning paths.
4. Comprehensive Evaluation: Through extensive experiments on open-source and closed-source LLMs, the paper demonstrates that the proposed methods effectively correct causal errors and significantly enhance reasoning capabilities.

Experimental Methodology

The research primarily leverages several datasets, including GSM8K, Math, OlympiadBench, and Omnimath, alongside LLMs such as Qwen and Deepseek variants. The effectiveness of the proposed methodologies is evaluated using Exact Match (EM) scores and causal metrics like the heterogeneous effect and factual average treatment effect, revealing how well the causalized CoT improves logical reasoning and accuracy.

Implications and Future Directions

The implications of this research are multifaceted. Practically, it provides a framework for enhancing LLM performance by ensuring the reasoning paths not only reach correct conclusions but do so through logically coherent steps. Theoretically, the paper lays the foundation for further exploration into causality within AI reasoning, suggesting that future investigations could delve into extending causal analyses to other forms of model interpretability. Such advancements could significantly impact various AI applications where understanding decision-making processes is crucial.

In the field of AI development, the insights gained from this causal perspective of CoT could inspire novel approaches ensuring AI systems produce not just accurate outputs, but also outputs that align with human logic and reasoning paradigms. This shift could be pivotal in creating trustworthy AI systems that operate transparently and autonomously across diverse domains, from automated reasoning in scientific research to practical decision-making in everyday applications.

Conclusion

In summary, this paper marks a significant step toward demystifying the intricate workings of LLMs' reasoning capabilities by modeling them causally. The introduction of causal methodologies in analyzing CoT mechanisms adds depth to LLM interpretability, paving the way for future advancements in AI reasoning and decision-making processes. The methodology's success in enhancing reasoning accuracy underscores its potential as a cornerstone in the continuous development of intelligent systems.