Making Reasoning Matter: Measuring Faithfulness in Chain-of-Thought Reasoning

Large Language Models (LLMs) have shown remarkable capabilities in complex reasoning tasks when prompted to generate step-by-step explanations, known as Chain-of-Thought (CoT) reasoning. However, a critical question remains: Do these models actually use their generated reasoning steps to arrive at their final answers?

The Problem

While CoT prompting improves performance on many reasoning tasks, recent studies suggest that models might not always rely on their intermediate reasoning steps. This raises concerns about the faithfulness of the reasoning process - whether the generated explanations truly reflect the model’s decision-making process.

Our Approach

In our paper “Making Reasoning Matter”, we introduce a comprehensive framework to measure and improve reasoning faithfulness using causal mediation analysis.

Key Contributions

Causal Framework: We develop a method to quantify how much intermediate reasoning steps causally influence final predictions
Extensive Evaluation: Analysis across 11 different language models on multiple reasoning tasks
Practical Improvements: Techniques to enhance reasoning faithfulness

Key Findings

Our causal mediation analysis across different model families reveals several important insights:

Model Behavior Varies by Training Objective

In-context learning and instruction-tuning improve alignment with reasoning chains
Models trained with RLHF show more direct effects than indirect effects, suggesting potential issues with faithful reasoning
Larger models don’t automatically show better faithfulness

Task-Specific Patterns

We evaluated on three types of reasoning tasks:

Mathematical Reasoning (GSM8K)
Strategic Reasoning (StrategyQA)
Causal Understanding

Results show that faithfulness varies significantly across task types, with mathematical reasoning showing different patterns compared to strategic reasoning tasks.

Methodology

Our approach uses causal mediation analysis to decompose the total effect of reasoning problems into:

Direct Effect: How much the problem directly influences the answer (bypassing reasoning)
Indirect Effect: How much the problem influences the answer through generated reasoning steps

High indirect effect indicates faithful reasoning, while high direct effect suggests the model might be ignoring its reasoning steps.

Implications

This work has important implications for:

Model Development: Understanding which training objectives promote faithful reasoning
Evaluation: Moving beyond accuracy to assess reasoning quality
Trust and Interpretability: Building more reliable and transparent AI systems

Future Directions

Our findings open several avenues for future research:

Developing training methods that promote faithfulness
Creating better evaluation metrics for reasoning quality
Understanding the relationship between model scale and reasoning faithfulness

Citation:

@misc{debjit2024frodo,
    title={Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning},
    author={Debjit Paul and Robert West and Antoine Bosselut and Boi Faltings},
    year={2024},
    eprint={2402.13950},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Links:

This post is part of our ongoing research into making AI reasoning more transparent and reliable. For more updates on our work, follow our research blog.