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Source note3 min read

CASE: Causal Alignment and Structural Enforcement for Improving Chain-of-Thought Faithfulness

Source

arXiv

arxiv.org/abs/2607.18820v1

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Plain-Language Brief

Researchers propose a new framework called CASE to improve the faithfulness of chain-of-thought (CoT) reasoning in large language models (LLMs). The goal is to ensure that the generated reasoning supports the final answer more accurately and doesn't take a direct instruction-to-answer shortcut. During training, CASE uses counterfactual-CoT, biased-instruction, and empty-instruction datasets, and applies selective-loss fine-tuning. During inference, it masks direct attention from instruction tokens to answer tokens. The results show that CASE outperforms the strongest baselines in terms of CoT faithfulness on three models and four benchmarks.

What the Document Says

  • The paper discusses a problem with chain-of-thought (CoT) reasoning in large language models (LLMs), where generated reasoning may not faithfully support the final answer.
  • A framework called CASE is proposed to address this issue. It combines training-time causal alignment and inference-time structural enforcement.
  • During training, CASE builds counterfactual-CoT, biased-instruction, and empty-instruction datasets, and applies selective-loss fine-tuning.
  • During inference, CASE masks direct attention from instruction tokens to answer tokens.
  • The research provides an information-theoretic analysis showing how these components promote faithful chains.
  • Experiments show that CASE achieves a 37% average per-setting relative improvement in overall CoT faithfulness over the strongest baselines on three models and four benchmarks.

Key Concepts Mentioned

  • chain-of-thought reasoning
  • large language models
  • causal alignment
  • structural enforcement
  • counterfactual-CoT
  • biased-instruction
  • empty-instruction datasets
  • selective-loss fine-tuning

Claims Made by the Document

  • The generated reasoning in chain-of-thought (CoT) may not faithfully support the final answer.
  • CASE framework addresses this issue by combining training-time causal alignment and inference-time structural enforcement.
  • During training, CASE builds specific datasets and applies selective-loss fine-tuning to strengthen CoT-to-answer dependence while suppressing instruction shortcuts.
  • During inference, CASE masks direct attention from instruction tokens to answer tokens.
  • CASE promotes faithful chains through the components mentioned.

Evidence Used

  • The paper discusses a problem with chain-of-thought (CoT) reasoning and proposes a solution.
  • The paper describes how CASE is designed to work, including the use of specific datasets during training and masking during inference.

Methods / Approach

  • training-time causal alignment
  • inference-time structural enforcement
  • counterfactual-CoT
  • biased-instruction
  • empty-instruction datasets
  • selective-loss fine-tuning
  • masking direct attention

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Chain-of-thought (CoT) reasoning is widely used to improve both the performance and interpretability of large language models (LLMs), yet the generated reasoning may not faithfully support the final answer. We study this problem from a causal perspective, where a faithful CoT process should follow the chain $Z\rightarrow X\rightarrow Y$, with $Z$, $X$, and $Y$ denoting the instruction, reasoning chain, and final answer, respectively. In this process, the instruction should affect the answer only through the reasoning chain. However, conventional autoregressive LLMs condition answer generation on both the instruction and the CoT, which still allows a direct instruction-to-answer shortcut. To address this issue, we propose CASE, a framework that combines training-time causal alignment and inference-time structural enforcement. During training, CASE builds counterfactual-CoT, biased-instruction, and empty-instruction datasets, and applies selective-loss fine-tuning to strengthen CoT-to-answer dependence while suppressing instruction shortcuts. During inference, CASE masks direct attention from instruction tokens to answer tokens, preventing the model from bypassing the generated CoT. We provide an information-theoretic analysis showing how these components promote faithful chains. Experiments on three models and four benchmarks show that CASE achieves a 37% average per-setting relative improvement in overall CoT faithfulness over the strongest baselines, exhibits stronger cross-dataset faithfulness transfer, and maintains competitive average accuracy. Code is available at https://github.com/oddwang/CASE.