datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MMMLU
Multilingual Massive Multitask Language Understanding (MMMLU)
The MMLU is a widely recognized benchmark of general knowledge attained by AI models. It covers a broad range of topics from 57 different categories, covering elementary-level knowledge up to advanced professional subjects like law, physics, history, and computer science.
We translated the MMLU’s test set into 14 languages using professional human translators. Relying on human translators for this evaluation increases… See the full description on the dataset page: https://huggingface.co/datasets/openai/MMMLU.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue line… See the full description on the dataset page: https://huggingface.co/datasets/bench-llm/or-bench.officeqa
OfficeQA
Dataset Summary
OfficeQA is a grounded reasoning benchmark by Databricks for evaluating model and agent performance on end-to-end reasoning over real-world documents.
The benchmark consists of question–answer pairs that require reasoning over historical U.S. Treasury Bulletin documents (1939–2025), which contain dense financial tables, charts, and narrative text. OfficeQA is designed to test retrieval, tool use, and multi-step reasoning in… See the full description on the dataset page: https://huggingface.co/datasets/databricks/officeqa.AA-Omniscience-Public
Public Dataset for AA-Omniscience: Evaluating Cross-Domain Knowledge Reliability in Large Language Models
AA-Omniscience-Public contains 600 questions across a wide range of domains used to test a model’s knowledge and hallucination tendencies.
Leaderboard and detailed results
Paper
Introduction
We introduce AA-Omniscience, a benchmark dataset designed to measure a model’s ability to both recall factual information accurately across domains, and correctly… See the full description on the dataset page: https://huggingface.co/datasets/ArtificialAnalysis/AA-Omniscience-Public.officeqa-pro-v2
OfficeQA Pro v2
Dataset Summary
OfficeQA Pro v2 is a grounded reasoning benchmark by Databricks for evaluating model and agent performance on end-to-end reasoning over real-world documents.
The benchmark consists of question–answer pairs that require reasoning over two centuries of U.S. Federal Accounts of Receipts and Expenditures reporting (1793–2024) — Combined Statements of Receipts, Outlays, and Balances of the United States Government, together with earlier… See the full description on the dataset page: https://huggingface.co/datasets/databricks/officeqa-pro-v2.thai-onet-m6-exam
Thai O-Net Exams Dataset
Overview
The Thai O-Net Exams dataset is a comprehensive collection of exam questions and answers from the Thai Ordinary National Educational Test (O-Net). This dataset covers various subjects for Grade 12 (M6) level, designed to assist in educational research and development of question-answering systems.
Dataset Source
Thai National Institute of Educational Testing Service (NIETS)
Maintainer
Dr. Kobkrit Viriyayudhakorn… See the full description on the dataset page: https://huggingface.co/datasets/matichon/thai-onet-m6-exam.thai-onet-m6-exam
Thai O-Net Exams Dataset
Overview
The Thai O-Net Exams dataset is a comprehensive collection of exam questions and answers from the Thai Ordinary National Educational Test (O-Net). This dataset covers various subjects for Grade 12 (M6) level, designed to assist in educational research and development of question-answering systems.
Dataset Source
Thai National Institute of Educational Testing Service (NIETS)
Maintainer
Dr. Kobkrit… See the full description on the dataset page: https://huggingface.co/datasets/openthaigpt/thai-onet-m6-exam.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue line… See the full description on the dataset page: https://huggingface.co/datasets/bench-llms/or-bench.OR-Space
OR-Space
A full-lifecycle workspace benchmark for industrial optimization agents.
OR-Space evaluates whether language-model agents can work reliably with
operations research problems represented as executable, multi-file workspaces.
Rather than presenting a self-contained mathematical prompt, each task
distributes evidence across business requirements, structured data, source
code, execution logs, and solver records.
The benchmark contains 100 optimization topologies. Each… See the full description on the dataset page: https://huggingface.co/datasets/Chenyu-Zhou/OR-Space.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our leaderboard at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue… See the full description on the dataset page: https://huggingface.co/datasets/orbench-llm/or-bench.or-bench-toxic-all
OR-Bench: An Over-Refusal Benchmark for Large Language Models
This dataset constains highly toxic prompts, use with caution!!!
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic… See the full description on the dataset page: https://huggingface.co/datasets/bench-llms/or-bench-toxic-all.Reverse-alpha-beta-no-outsideOracleProto
OracleProto: Forecasting Evaluation Set
Chinese doc: [中文文档]
GitHub repo: [MaYiding/OracleProto]
Visit Our Leaderboards: [Website]
View Our Paper: [arXiv]
A SQLite-packaged evaluation set of 80 hand-curated forecasting questions on real-world events, with resolution dates between 2026-03-12 and 2026-04-14, released alongside the GitHub Repo. Both the rows and the byte-stable prompt-reconstruction recipe are packaged in a single file, forecast_eval_set_example.db, which exposes two… See the full description on the dataset page: https://huggingface.co/datasets/MaYiding/OracleProto.Original-alpha-suppression-task-boosttiny-singleturn-chat-kotrilemma-of-truth
Dataset Card for Trilemma of Truth (ToT) Dataset
🧾 Dataset Summary
The Trilemma of Truth (ToT) dataset serves as a benchmark for evaluating veracity probes across three distinct statement types:
Factually true statements.
Factually false statements.
Neither-valued statements are defined as those for which the language model lacks sufficient evidence to assign a truth value (see formal definition below).
The dataset includes three domain configurations:… See the full description on the dataset page: https://huggingface.co/datasets/carlomarxx/trilemma-of-truth.OmniBrainBench
OmniBrainBench
🍎 Homepage|💻 GitHub|🤗 Dataset|📖 Paper
This repository is the official implementation of the paper [OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks].
🚀 News
[02/2026] Our OmniBrainBench is accepted by CVPR2026!
[12/2025] We have released the evaluation code and dataset for OmniBrainBench.
[11/2025] The manuscript can be found on arXiv.
🚀Overview
we introduce… See the full description on the dataset page: https://huggingface.co/datasets/FrankPN/OmniBrainBench.Original-no-persona-replacement-remainderdaily-oracle
Daily Oracle
📰 Project Website📝 Paper - Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle
Daily Oracle is a continuous evaluation benchmark using automatically generated QA pairs from daily news to assess how the future prediction capabilities of LLMs evolve over time.
Dataset Details
Question Type: True/False (TF) & Multiple Choice (MC)
Current Version*
Time Span: 2020.01.01 - 2026.07.18
Size: 20,376 TF questions and 18,557 MC… See the full description on the dataset page: https://huggingface.co/datasets/agentic-learning-ai-lab/daily-oracle.Original-hybrid-shared-no-persona-remainderOriginal-hybrid-correct-train-no-persona-meanMulti-Opthalingua
Cite
Accepted to AAAI 2025 (https://openreview.net/group?id=AAAI.org/2025/Conference#tab-recent-activity)
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs:
@misc{restrepo2024multiophthalinguamultilingualbenchmarkassessing,
title={Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs},
author={David Restrepo and Chenwei Wu and Zhengxu Tang and Zitao Shuai and Thao… See the full description on the dataset page: https://huggingface.co/datasets/AAAIBenchmark/Multi-Opthalingua.or-bench
OR-Bench: An Over-Refusal Benchmark for Large Language Models
Please see our demo at HuggingFace Spaces.
Overall Plots of Model Performances
Below is the overall model performance. X axis shows the rejection rate on OR-Bench-Hard-1K and Y axis shows the rejection rate on OR-Bench-Toxic. The best aligned model should be on the top left corner of the plot where the model rejects the most number of toxic prompts and least number of safe prompts. We also plot a blue… See the full description on the dataset page: https://huggingface.co/datasets/jerogo/or-bench.Original-hybrid-train-no-persona-meanOriginal-circuit-discoveryInstitutional-Information-of-Bangladesh
Institutional-Information-of-Bangladesh Dataset
This Dataset contains all verified and authorized Institutional information in Bangladesh
Description
I have collected all data from bangladeshi government authorized web portal and also shared this link in the data source section,
this dataset is sutitable for various NLP tasks
Data Source
http://data.gov.bd/
Dataset Card Authors
Mahadi Hassan
Dataset Card Contact… See the full description on the dataset page: https://huggingface.co/datasets/Mahadih534/Institutional-Information-of-Bangladesh.thai-investment-consultant-licensing-exams
Thai Public Investment Consultant (IC) Exams Dataset
Overview
This dataset comprises a collection of exam questions and answers from the Thai Public Investment Consultant (IC) Examinations. It's a valuable resource for developing and evaluating question-answering systems in the finance sector.
Dataset Source
The Stock Exchange of Thailand (SET)
Maintainer
Dr. Kobkrit Viriyayudhakorn
Email: kobkrit@iapp.co.th
Dataset Description
This… See the full description on the dataset page: https://huggingface.co/datasets/openthaigpt/thai-investment-consultant-licensing-exams.AA-Omniscience-Public
Public Dataset for AA-Omniscience: Evaluating Cross-Domain Knowledge Reliability in Large Language Models
AA-Omniscience-Public contains 600 questions across a wide range of domains used to test a model’s knowledge and hallucination tendencies.
Leaderboard and detailed results
Paper
Introduction
We introduce AA-Omniscience, a benchmark dataset designed to measure a model’s ability to both recall factual information accurately across domains, and correctly… See the full description on the dataset page: https://huggingface.co/datasets/Sandhya1912/AA-Omniscience-Public.R1-Onevision-Bench
R1-Onevision-Bench
[📂 GitHub][📝 Paper]
[🤗 HF Dataset] [🤗 HF Model] [🤗 HF Demo]
Dataset Overview
R1-Onevision-Bench comprises 38 subcategories organized into 5 major domains, including Math, Biology, Chemistry, Physics, Deducation. Additionally, the tasks are categorized into five levels of difficulty, ranging from ‘Junior High School’ to ‘Social Test’ challenges, ensuring a comprehensive evaluation of model capabilities across varying complexities.… See the full description on the dataset page: https://huggingface.co/datasets/Fancy-MLLM/R1-Onevision-Bench.Original-baseline-bias-unbias