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Cutting Through the Noise: Boosting LLM Performance on Math Word Problems

Summary

The paper studies how large language models solving math word problems are thrown off by irrelevant numbers in the problem text. The authors use a two-step prompting chain: a model first invents an unrelated physical quantity (such as volume, temperature or speed) with start and end values, then weaves it into the problem so that the solution and answer stay unchanged; constraints ensure the new variable has a different unit and adds no information about the original quantities. This yields PROBLEMATHIC, a dataset of paired clean and noisy problems at two difficulty levels (simple additive problems and complex ones with nested operations), with hand-written noisy test items, plus GSM-8K-Adv, a noisy variant of GSM8K. Distractors cut accuracy by about a quarter on average across models. Fine-tuning open models on noisy examples with explanations that point out the irrelevant variables improves accuracy on noisy problems by about 8%. The main finding is that models tend to use every number in the problem mechanically.

How Mova uses it

Effectiveness in Mova

Measured: SVAMP 57.3% → 52.7% (identical in two runs), i.e. a loss, so the idea was not adopted. Diagnosis: the artificial distractor always sits in the same position, so the model learns the position rather than the principle; in the paper, distractors are tailored to the problem type (similar object, same topic).

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Cite

The paper itself, not this card. BibTeX from the library's record of the work. arXiv 2406.15444

@misc{anantheswaran2024cutting,
  title = {{Cutting Through the Noise: Boosting LLM Performance on Math Word Problems}},
  author = {Anantheswaran, Ujjwala and Gupta, Himanshu and Scaria, Kevin and Verma, Shreyas and Baral, Chitta and Mishra, Swaroop},
  year = {2024},
  eprint = {2406.15444},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2406.15444}
}

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