Dataset Card for GPQA

GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google. We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation model… See the full description on the dataset page: https://huggingface.co/datasets/Wanfq/gpqa.

Dataset Card for GPQA

Formatted version of original GPQA dataset. This removes most columns and adds single columns options and answer to contain a list of the possible answers and the index of the correct one. GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy… See the full description on the dataset page: https://huggingface.co/datasets/jeggers/gpqa_formatted.

GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.

lihaoxin2020/llama-synthetic_1-math_only-gpqa-questions-r1 dataset hosted on Hugging Face and contributed by the HF Datasets community

lihaoxin2020/qwen-synthetic_1-math_only-gpqa-questions-r1 dataset hosted on Hugging Face and contributed by the HF Datasets community

ko-gpqa

ko-gpqa is a Korean-translated version of the GPQA (Graduate-Level Google‑Proof Q&A) benchmark dataset, which consists of high-difficulty science questions. Introduced in this paper, GPQA is designed to go beyond simple fact retrieval and instead test an AI system’s ability to perform deep understanding and logical reasoning. It is particularly useful for evaluating true comprehension and inference capabilities in language models. The Korean translation was performed using… See the full description on the dataset page: https://huggingface.co/datasets/davidkim205/ko-gpqa.

gpqa_formatted Dataset (TR)

  Dataset Overview

This is the TR version of jeggers/gpqa_formatted dataset which is formatted version of the original GPQA dataset. GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.… See the full description on the dataset page: https://huggingface.co/datasets/FurkyT/gpqa_formatted_TR.

Dataset Contains Solution to GPQA , Provided by Gemini-2.0-Thinking Pass@1 , Prompt Engineered to Align better to solve scintefic questions

  ACCURACY: 64.65





  Correct-Questions: 353

Verified Using Gemini-2.0

Rated(max 10) by Gemini-2.0

![Results](data:image/png;base64… See the full description on the dataset page: https://huggingface.co/datasets/fhai50032/GPQA-Verified-Thinking-O1-Rated.

GPQA with Llama-3.1-70B-Instruct

This dataset contains 646 graduate-level science questions from the GPQA benchmark with 100 candidate responses generated by Llama-3.1-70B-Instruct for each problem. Each response has been evaluated for correctness using a mixture of GPT-4o-mini and procedural Python code to robustly parse different answer formats, and scored by multiple reward models (scalar values) and LM judges (boolean verdicts).

  Dataset Structure

Split: Single… See the full description on the dataset page: https://huggingface.co/datasets/hazyresearch/GPQA_with_Llama_3.1_70B_Instruct_v1.

ReasonSet Dataset

This dataset is sourced from the paper ["REL: Working Out Is All You Need"].

  Dataset Description

ReasonSet is a dataset of problems and their worked solutions, specifically designed to help improve models reasoning abilities. Questions are sourced from AIME, GPQA, MATH and some hand created ones

Question: The question Working out: Indepth solution with reasoning steps provided_solution: Provided Solution by the benchmark

  Citation

If you use… See the full description on the dataset page: https://huggingface.co/datasets/TamasSimonds/ReasonSet.