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Grade-School Math with Irrelevant Context (GSM-IC) benchmark is an arithmetic reasoning dataset built upon GSM8K, by adding irrelevant sentences in problem descriptions. GSM-IC is constructed to evaluate the distractibility of language models.

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Grade-School Math with Irrelevant Context (GSM-IC)

This repository contains the dataset Grade-School Math with Irrelevant Context (GSM-IC) used in this paper: Large Language Models Can Be Easily Distracted by Irrelevant Context.

Data Format

  • GSM8K_validation.jsonl: the development split of GSM8K dataset used in the experiments.
Field name Value
question Input question.
answer The ground truth answer.
n_steps The number of intermediate steps to calculate the answer.
  • GSM-IC_2step.json: GSM-IC split with problems that require 2 intermediate steps.
Field name Value
original_question Original question from the GSM8K development set.
new_question The new question with irrelevant context added to the original question.
answer The ground truth answer.
n_steps The number of intermediate steps to calculate the answer.
role_label, number_label, sentence_label Categories of the added irrelevant context. Needed for result analysis, not needed for model prediction.
role, number, sentence_template Added irrelevant context. Not needed for experiments.
  • GSM-IC_mstep.json: GSM-IC split with problems that require more than 2 intermediate steps. Same format as GSM-IC_2step.json.

Citation

If you use the data released through this repository, please cite the following paper:

@article{shi2023large,
  title={Large Language Models Can Be Easily Distracted by Irrelevant Context},
  author={Shi, Freda and Chen, Xinyun and Misra, Kanishka and Scales, Nathan and Dohan, David and Chi, Ed and Schärli, Nathanael and Zhou, Denny},
  journal={arXiv preprint arXiv:2302.00093},
  year={2023}
}

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Grade-School Math with Irrelevant Context (GSM-IC) benchmark is an arithmetic reasoning dataset built upon GSM8K, by adding irrelevant sentences in problem descriptions. GSM-IC is constructed to evaluate the distractibility of language models.

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