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This paper studies the performance improvement of large-scale language models (LLMs) through Chain-of-Thought (CoT) prompting from a data distribution perspective. We investigate whether CoT inference reflects structural inductive biases learned from training data, or whether its effectiveness is limited by the degree of distributional mismatch between the training and test questions. To analyze CoT inference across three dimensions—task, length, and format—we designed and used DataAlchemy, a controlled environment in which LLMs are trained from scratch and systematically examined under various distributional conditions. Our results reveal that CoT inference is a fragile phenomenon that disappears when the training distribution deviates. Therefore, we emphasize that achieving truly generalizable inference remains a challenging task.
Takeaways, Limitations
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Takeaways: We show that CoT inference is highly dependent on the training data distribution, and its performance degrades sharply for data with distributions different from the training data. This suggests the limitations of CoT inference and the lack of true inference capability. We present a novel methodology for systematically evaluating the inference capability of LLMs in a controlled environment such as DataAlchemy.
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Limitations: The DataAlchemy environment presents experimental results under specific conditions, so further research is needed to determine generalizability to complex real-world environments. This study highlights the weaknesses of CoT inference, but lacks discussion of the advantages of CoT prompting or other areas for improvement. Because these results may be limited to specific types of LLMs and datasets, further research on other models and datasets is needed.
