{"url":"/dataset/mgsm","name":"MGSM","full_name":"Multilingual Grade School Math","description_markdown":"Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems. The same 250 problems from GSM8K are each translated via human annotators in 10 languages. GSM8K (Grade School Math 8K) is a dataset of 8.5K high-quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.","description_withheld":null,"homepage":"https://github.com/google-research/url-nlp/tree/main/mgsm","introduced_date":"2022-10-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/language-models-are-multilingual-chain-of","title":"Language Models are Multilingual Chain-of-Thought Reasoners","first_author":"Freda Shi","url":null},"license":{"name":"Creative Commons Attribution-ShareAlike 4.0 International Public License","url":"https://github.com/google-research/url-nlp/blob/main/LICENSE"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Math Word Problem Solving","url":"/task/math-word-problem-solving","datasets_with_task":"/datasets/task/math-word-problem-solving"},{"name":"Multi-task Language Understanding","url":"/task/multi-task-language-understanding","datasets_with_task":"/datasets/task/multi-task-language-understanding"},{"name":"Arithmetic Reasoning","url":"/task/arithmetic-reasoning","datasets_with_task":"/datasets/task/arithmetic-reasoning"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"French","url":"/datasets/language/french"},{"name":"Spanish","url":"/datasets/language/spanish"},{"name":"German","url":"/datasets/language/german"},{"name":"Chinese","url":"/datasets/language/chinese"},{"name":"Bengali","url":"/datasets/language/bengali"},{"name":"Japanese","url":"/datasets/language/japanese"},{"name":"Russian","url":"/datasets/language/russian"},{"name":"Telugu","url":"/datasets/language/telugu"},{"name":"Thai","url":"/datasets/language/thai"},{"name":"Swahili","url":"/datasets/language/swahili"}],"variants":["MGSM"],"data_loaders":[],"num_papers_in_archive":107,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multi-task-language-understanding-on-mgsm","task":"Multi-task Language Understanding","dataset_variant":"MGSM","rows":12,"metrics":["Average (%)"],"first_row_in_archive_order":{"model":"PaLM 2 (few-shot, k=8, SC)","paper":"/paper/palm-2-technical-report-1","metrics":{"Average (%)":"87.0"},"code_links":[{"title":"eternityyw/tram-benchmark","url":"https://github.com/eternityyw/tram-benchmark"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/palm-2-technical-report-1","title":"PaLM 2 Technical Report","date":"2023-05-17","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/transcending-scaling-laws-with-0-1-extra","title":"Transcending Scaling Laws with 0.1% Extra Compute","date":"2022-10-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/scaling-instruction-finetuned-language-models","title":"Scaling Instruction-Finetuned Language Models","date":"2022-10-20","rows_on_this_dataset":8,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":8,"samples_unverified":9,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/palm-scaling-language-modeling-with-pathways-1","title":"PaLM: Scaling Language Modeling with Pathways","date":"2022-04-05","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":37,"samples_ran":30,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":54,"samples_ran":38,"samples_unverified":16,"pointer_only_for_licence":2,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}