{"url":"/dataset/aqua-rat","name":"AQUA-RAT","full_name":"Algebra Question Answering with Rationales","description_markdown":"Algebra Question Answering with Rationales (AQUA-RAT) is a dataset that contains algebraic word problems with rationales. The dataset consists of about 100,000 algebraic word problems with natural language rationales. Each problem is a json object consisting of four parts:\r\n* question - A natural language definition of the problem to solve\r\n* options - 5 possible options (A, B, C, D and E), among which one is correct\r\n* rationale - A natural language description of the solution to the problem\r\n* correct - The correct option\r\n\r\nSource: [https://github.com/deepmind/AQuA](https://github.com/deepmind/AQuA)","description_withheld":null,"homepage":"https://github.com/deepmind/AQuA","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/program-induction-by-rationale-generation","title":"Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems","first_author":"Wang Ling","url":null},"license":{"name":"Apache 2.0","url":"https://www.apache.org/licenses/LICENSE-2.0"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Natural Language Inference","url":"/task/natural-language-inference","datasets_with_task":"/datasets/task/natural-language-inference"},{"name":"Natural Language Understanding","url":"/task/natural-language-understanding","datasets_with_task":"/datasets/task/natural-language-understanding"},{"name":"Program induction","url":"/task/program-induction","datasets_with_task":"/datasets/task/program-induction"}],"languages":[],"variants":["AQUA-RAT"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/deepmind/aqua_rat","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/aqua_rat","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/facebookresearch/ParlAI","url":"https://parl.ai/docs/tasks.html#aqua","frameworks":["pytorch"]},{"repo":"https://github.com/deepmind/AQuA","url":"https://github.com/deepmind/AQuA","frameworks":[]}],"num_papers_in_archive":64,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}