A Multilingual Dataset (MultiMWP) and Benchmark for Math Word Problem Generation
Why this work is in the frame
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Bibliographic record
Abstract
We present a multi-way parallel corpus of Math Word Problems (MWPs) in nine languages, including six low-resource languages. To date, this is the largest multilingual MWP dataset available. We utilize this dataset and show the viability of using pre-trained multilingual sequence-sequence language models (prMSLMs) for autoregressive MWP generation in both monolingual and multilingual setups, particularly for low-resource languages. We also integrate a math constraint satisfaction module with autoregressive text generation. Our extensive evaluations identify several factors that affect autoregressive text generation on prMSLMs. These include language representation in the model, model size, existence of similar languages in the model, and language script. Overall, our results reveal that autoregressive MWP generation on top of prMSLMs is very promising, even for low-resource languages.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it