Datasets:

allenai
/

omega-500

license: mit
dataset_info:
  - config_name: default
    features:
      - name: id
        dtype: string
      - name: original_id
        dtype: string
      - name: family
        dtype: string
      - name: difficulty_level
        dtype: int64
      - name: source_family
        dtype: string
      - name: source_level
        dtype: string
      - name: messages
        list:
          - name: role
            dtype: string
          - name: content
            dtype: string
      - name: ground_truth
        dtype: string
      - name: dataset
        dtype: string
    splits:
      - name: train
        num_bytes: 100815
        num_examples: 500
    download_size: 100815
    dataset_size: 100815
configs:
  - config_name: default
    data_files:
      - split: train
        path: train-*

Omega-500: Random Sample of Mathematical Problems

This dataset contains a random sample of 500 mathematical problems selected from the comprehensive OMEGA problem families dataset. It provides a diverse, manageable subset for quick evaluation and experimentation across multiple mathematical domains and difficulty levels.

Overview

Omega-500 is designed for:

Quick Evaluation: Fast assessment of model capabilities across math domains
Prototyping: Testing new approaches before scaling to larger datasets
Benchmarking: Standardized subset for fair model comparisons
Research: Focused analysis on a balanced mathematical problem set

The sample maintains diversity across mathematical domains and difficulty levels while keeping the dataset size manageable for rapid iteration.

Quick Start

from datasets import load_dataset

# Load the Omega-500 sample
dataset = load_dataset("allenai/omega-500")
problems = dataset["train"]

# Access individual problems
first_problem = problems[0]
print("Problem:", first_problem["messages"][0]["content"])
print("Answer:", first_problem["ground_truth"])
print("Family:", first_problem["family"])
print("Difficulty:", first_problem["difficulty_level"])

Dataset Composition

Total Problems: 500

Domain Distribution:

Algebra: 92 problems (18.4%)
Arithmetic: 173 problems (34.6%)
Combinatorics: 84 problems (16.8%)
Geometry: 45 problems (9.0%)
Logic: 61 problems (12.2%)
Number Theory: 45 problems (9.0%)

Data Fields

Each problem contains:

id: Unique identifier for this sample
original_id: Original identifier from source dataset
family: Problem family (e.g., "algebra_func_area")
difficulty_level: Numeric difficulty level from source
source_family: Source family directory name
source_level: Source difficulty level name
messages: Problem statement in chat format
ground_truth: Correct answer
dataset: Dataset identifier ("OMEGA_500_SAMPLE")

Citation

If you use this dataset, please cite the original OMEGA work:

@article{sun2024omega,
  title     = {OMEGA: Can LLMs Reason Outside the Box in Math? Evaluating Exploratory, Compositional, and Transformative Generalization},
  author    = {Yiyou Sun and Shawn Hu and Georgia Zhou and Ken Zheng and Hannaneh Hajishirzi and Nouha Dziri and Dawn Song},
  journal   = {arXiv preprint arXiv:2506.18880},
  year      = {2024},
}

Related Resources

Full Problem Families: See omega-problems for the complete dataset
Explorative Dataset: See omega-explorative for explorative reasoning challenges
Compositional Dataset: See omega-compositional for compositional reasoning challenges
Transformative Dataset: See omega-transformative for transformative reasoning challenges
Paper: See the full details in paper
Code Repository: See generation code on github