Dataset Card for AQUA-RAT

  Dataset Summary

A large-scale dataset consisting of approximately 100,000 algebraic word problems. The solution to each question is explained step-by-step using natural language. This data is used to train a program generation model that learns to generate the explanation, while generating the program that solves the question.

  Supported Tasks and Leaderboards





  Languages

en

  Dataset Structure





  Data Instances… See the full description on the dataset page: https://huggingface.co/datasets/deepmind/aqua_rat.

Dataset Card for Calc-aqua_rat

  Summary

This dataset is an instance of AQuA-RAT dataset extended with in-context calls of a sympy calculator.

  Supported Tasks

The dataset is intended for training Chain-of-Thought reasoning models able to use external tools to enhance the factuality of their responses. This dataset presents in-context scenarios where models can outsource the computations in the reasoning chain to a calculator.

  Construction Process

The… See the full description on the dataset page: https://huggingface.co/datasets/MU-NLPC/Calc-aqua_rat.

AQUA-RAT MCQA Dataset

This dataset contains the AQUA-RAT dataset converted to Multiple Choice Question Answering (MCQA) format with modifications.

  Dataset Description

AQUA-RAT is a dataset of algebraic word problems with rationales. This version has been processed to:

Remove all questions where the correct answer was option "E" (5th choice) Remove the "E" option from all remaining questions (4 choices: A, B, C, D) Merge validation and test splits into a single test split… See the full description on the dataset page: https://huggingface.co/datasets/RikoteMaster/aqua-rat-mcqa.

Dataset

This dataset was created by Việt Hưng Nguyễn

Released under Apache 2.0

Contents

laurentiubp/aqua-rat dataset hosted on Hugging Face and contributed by the HF Datasets community

Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset. AQuA-RAT has provided the questions, options, rationale, and the correct options.

MNLP M2 MCQA Dataset

A unified multiple-choice question answering (MCQA) benchmark on STEM subjects combining samples from OpenBookQA, SciQ, MMLU-auxiliary, AQUA-Rat, and MedMCQA.

  Dataset Summary

This dataset merges five existing science and knowledge-based MCQA datasets into one standardized format:

Source Train samples

OpenBookQA 4 900

SciQ 10 000

MMLU-aux 85 100

AQUA-Rat 50 000

MedMCQA 50 000

Total 200 000

  Supported Tasks and… See the full description on the dataset page: https://huggingface.co/datasets/NicoHelemon/MNLP_M2_mcqa_dataset.

Model Name

Params

MMLU-Pro-Plus Baseline Drop MMLU-Pro Baseline Drop Added Exp MMLU Pro Plus Added MMLU-redux 2.0 Baseline Drop AQUA-RAT Baseline Drop

CohereLabs/c4ai-command-a-03-2025 111B ✅ (single inference) ✅ done ✅ (HF naive batch) ✅ done ✅ done

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google/gemma-3-12b-it 12B ✅ (HF naive batch) ✅ done ✅ (HF naive batch) ✅ done ✅ done

✅

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meta-llama/Llama-4-Scout-17B-16E 17B ✅ (HF naive batch) ✅ done ✅ (HF naive batch) ✅ done ✅ done

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Qwen/Qwen3-4B 4B… See the full description on the dataset page: https://huggingface.co/datasets/sleeping-ai/Judgement-baseline.