Papers › An Independent Evaluation of ChatGPT on Mathematical Word Problems (MWP)

An Independent Evaluation of ChatGPT on Mathematical Word Problems (MWP)

23 Feb 2023arXiv:2302.13814archive 2025-07-28

Paulo Shakarian, Abhinav Koyyalamudi, Noel Ngu, Lakshmivihari Mareedu

We study the performance of a commercially available large language model (LLM) known as ChatGPT on math word problems (MWPs) from the dataset DRAW-1K. To our knowledge, this is the first independent evaluation of ChatGPT. We found that ChatGPT's performance changes dramatically based on the requirement to show its work, failing 20% of the time when it provides work compared with 84% when it does not. Further several factors about MWPs relating to the number of unknowns and number of operations that lead to a higher probability of failure when compared with the prior, specifically noting (across all experiments) that the probability of failure increases linearly with the number of addition and subtraction operations. We also have released the dataset of ChatGPT's responses to the MWPs to support further work on the characterization of LLM performance and present baseline machine learning models to predict if ChatGPT can correctly answer an MWP. We have released a dataset comprised of ChatGPT's responses to support further research in this area.

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Scan_Answers lab-v2/chatgpt_mwp_eval/data_with_implementation/source/JSONGPT.py official repository unverified CC0-1.0 (permissive) · 5e31e54789ef21f4 · report
Scan_Answers lab-v2/chatgpt_mwp_eval/data_with_implementation/source/trailGPT.py official repository unverified CC0-1.0 (permissive) · e8c0ea9158bf8877 · report
compare_answers lab-v2/chatgpt_mwp_eval/data_with_implementation/source/JSONGPT.py official repository unverified CC0-1.0 (permissive) · 7392bcd76e77496b · report
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Language ModelingLanguage ModellingLarge Language ModelMath

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