Masked Thought: Simply Masking Partial Reasoning Steps Can Improve
  Mathematical Reasoning Learning of Language Models
Changyu Chen Xiting Wang Ting-En Lin Ang Lv Yuchuan Wu Xin Gao Ji-Rong Wen Rui Yan Yongbin Li
In reasoning tasks, even a minor error can cascade into inaccurate results,
leading to suboptimal performance of large language models in such domains.
Earlier fine-tuning approaches sought to mitigate this by leveraging more
precise supervisory signals from human labeling, larger models, or
self-sampling, although at a high cost. Conversely, we develop a method that
avoids external resources, relying instead on introducing perturbations to the
input. Our training approach randomly masks certain tokens within the chain of
thought, a technique we found to be particularly effective for reasoning tasks.
When applied to fine-tuning with GSM8K, this method achieved a 5% improvement
in accuracy over standard supervised fine-tuning with a few codes modified and
no additional labeling effort. Furthermore, it is complementary to existing
methods. When integrated with related data augmentation methods, it leads to an
average improvement of 3% improvement in GSM8K accuracy and 1% improvement in
MATH accuracy across five datasets of various quality and size, as well as two
base models. We further investigate the mechanisms behind this improvement
through case studies and quantitative analysis, suggesting that our approach
may provide superior support for the model in capturing long-distance
dependencies, especially those related to questions. This enhancement could
deepen understanding of premises in questions and prior steps. Our code is
available at Github.