datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
gpqa
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… See the full description on the dataset page: https://huggingface.co/datasets/Idavidrein/gpqa.prompts.chat
a.k.a. Awesome ChatGPT Prompts
This is a Dataset Repository mirror of prompts.chat — a social platform for AI prompts.
📢 Notice
This Hugging Face dataset is a mirror. For the latest prompts, features, and community contributions, please visit:
🌐 Website: prompts.chat
📦 GitHub: github.com/f/awesome-chatgpt-prompts
About
prompts.chat is an open-source platform where users can share, discover, and collect AI prompts from the community. The project can… See the full description on the dataset page: https://huggingface.co/datasets/fka/prompts.chat.tiny-shakespeare
Data source
Downloaded via Andrej Karpathy's nanogpt repo from this link
Data Format
The entire dataset is split into train (90%) and test (10%).
All rows are at most 1024 tokens, using the Llama 2 tokenizer.
All rows are split cleanly so that sentences are whole and unbroken.
PersonaMem-v2
PersonaMem-v2: Towards Personalized Intelligence via Learning Implicit User Personas and Agentic Memory
📅 We have now released PersonaMem-v3!
🚨 The paper is now released. View the full paper here and codebase here.
Personalization is becoming the next milestone of artificial super-intelligence. AI cannot always satisfy every user, especially on tasks with subjective goals, but personalization offers a path toward pluralistic alignment.… See the full description on the dataset page: https://huggingface.co/datasets/bowen-upenn/PersonaMem-v2.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.PUPAThis dataset contains the data presented in the paper PAPILLON: Privacy Preservation from Internet-based and Local Language Model Ensembles.
Code: https://github.com/siyan-sylvia-li/PAPILLON
wildjailbreak
WildJailbreak Dataset Card
WildJailbreak is an open-source synthetic safety-training dataset with 262K vanilla (direct harmful requests) and adversarial (complex adversarial jailbreaks) prompt-response pairs. In order to mitigate exaggerated safety behaviors, WildJailbreaks provides two contrastive types of queries: 1) harmful queries (both vanilla and adversarial) and 2) benign queries that resemble harmful queries in form but contain no harmful intent.
Vanilla Harmful: direct… See the full description on the dataset page: https://huggingface.co/datasets/allenai/wildjailbreak.dialogsum
Dataset Card for DIALOGSum Corpus
Dataset Description
Links
Homepage: https://aclanthology.org/2021.findings-acl.449
Repository: https://github.com/cylnlp/dialogsum
Paper: https://aclanthology.org/2021.findings-acl.449
Point of Contact: https://huggingface.co/knkarthick
Dataset Summary
DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding… See the full description on the dataset page: https://huggingface.co/datasets/knkarthick/dialogsum.air-bench-2024
AIRBench 2024
AIRBench 2024 is a AI safety benchmark that aligns with emerging government
regulations and company policies. It consists of diverse, malicious prompts
spanning categories of the regulation-based safety categories in the
AIR 2024 safety taxonomy.
Dataset Details
Dataset Description
AIRBench 2024 is a AI safety benchmark that aligns with emerging government
regulations and company policies. It consists of diverse, malicious prompts
spanning… See the full description on the dataset page: https://huggingface.co/datasets/stanford-crfm/air-bench-2024.leetcode-problem-solutions
LeetCode Solution Dataset
This dataset contains community-contributed LeetCode solutions scraped from public discussions and solution pages, enriched with metadata such as vote counts, author info, tags, and full code content. The goal is to make high-quality, peer-reviewed coding solutions programmatically accessible for research, analysis, educational use, or developer tooling.
Column Descriptions
Column Name
Type
Description
question_slug
string
The unique… See the full description on the dataset page: https://huggingface.co/datasets/kaysss/leetcode-problem-solutions.spreadsheet-arena-release
Spreadsheet Arena
A dataset of 555 pairwise human preference votes over LLM-generated spreadsheets, spanning 124 distinct user-submitted prompts and 17 models.
This is the public release accompanying the Spreadsheet Arena paper.
Contents
battles.csv
models.csv
outputs/<id>/
sheet.json
sheet.xlsx
<id> is a 16-char hex identifier (HMAC-SHA256 of an internal UUID under a… See the full description on the dataset page: https://huggingface.co/datasets/Longitude-Labs/spreadsheet-arena-release.JailBreakV-28k
⛓💥 JailBreakV-28K: A Benchmark for Assessing the Robustness of MultiModal Large Language Models against Jailbreak Attacks
🌐 GitHub | 🛎 Project Page ｜ 👉 Download full datasets
If you like our project, please give us a star ⭐ on Hugging Face for the latest update.
📰 News
Date
Event
2024/07/09
🎉 Our paper is accepted by COLM 2024.
2024/06/22
🛠️ We have updated our version to V0.2, which supports users to customize their attack models… See the full description on the dataset page: https://huggingface.co/datasets/JailbreakV-28K/JailBreakV-28k.ChatGPT-Jailbreak-Prompts
Dataset Card for Dataset Name
Name
ChatGPT Jailbreak Prompts
Dataset Summary
ChatGPT Jailbreak Prompts is a complete collection of jailbreak related prompts for ChatGPT. This dataset is intended to provide a valuable resource for understanding and generating text in the context of jailbreaking in ChatGPT.
Languages
[English]
migration-bench-java-full
MigrationBench
1. 📖 Overview
🤗 MigrationBench
is a large-scale code migration benchmark dataset at the repository level,
across multiple programming languages.
Current and initial release includes java 8 repositories with the maven build system… See the full description on the dataset page: https://huggingface.co/datasets/AmazonScience/migration-bench-java-full.XSTest
XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models
XSTest is a test suite designed to identify exaggerated safety / false refusal in Large Language Models (LLMs).
It comprises 250 safe prompts across 10 different prompt types, along with 200 unsafe prompts as contrasts.
The test suite aims to evaluate how well LLMs balance being helpful with being harmless by testing if they unnecessarily refuse to answer safe prompts that superficially… See the full description on the dataset page: https://huggingface.co/datasets/Paul/XSTest.SimpleSafetyTestsTravelPlanner
TravelPlanner Dataset
TravelPlanner is a benchmark crafted for evaluating language agents in tool-use and complex planning within multiple constraints. (See our paper for more details.)
Introduction
In TravelPlanner, for a given query, language agents are expected to formulate a comprehensive plan that includes transportation, daily meals, attractions, and accommodation for each day.
TravelPlanner comprises 1,225 queries in total. The number of days and hard constraints… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/TravelPlanner.tatoeba_mt
Dataset Card for The Tatoeba Translation Challenge
Please note that this dataset is intended strictly for evaluation and benchmarking purposes. Training models on this dataset, or including it in automatically collected web-scale training corpora, may lead to benchmark contamination and invalidate evaluation results.
Dataset Summary
The Tatoeba Translation Challenge is a multilingual data set of machine translation benchmarks derived from user-contributed… See the full description on the dataset page: https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt.aopsICPC_Data
ICPC World Finals — a discriminative subset, with model traces
24 ICPC World Finals problems (2021–2025), together with 1440 full contest transcripts
of an LLM attempting them under simulated contest rules across three arms: with no hint,
with the official editorial as a hint, and with a hint written by a second model that gets
10 rounds of measured feedback to improve it.
Selection
The agent
Every contest run in this dataset comes from:… See the full description on the dataset page: https://huggingface.co/datasets/xupy21/ICPC_Data.openvino-arc140v-lunarlake
OpenVINO local-inference on an Intel Arc 140V (Lunar Lake) iGPU
Reference performance data for running local models on a single Intel Core Ultra 7 258V
(Lunar Lake) laptop with the integrated Intel Arc 140V (Xe2) GPU, via OpenVINO. All
inference runs on the iGPU; the NPU stays idle throughout, confirmed by the telemetry here.
This is reference characterization shared by a non-expert contributor — careful measurements
on one machine, offered so others can compare and correct, not… See the full description on the dataset page: https://huggingface.co/datasets/blairducrayoppat/openvino-arc140v-lunarlake.wa-hls4ml-projects
Dataset Card for wa-hls4ml Benchmark Dataset
The wa-hls4ml projects dataset, comprized of the Vivado/Vitis projects of neural networks converted into HLS Code via hls4ml. Projects are complete, and include all logs, HLS Code, VHDL Code, Intermediete Representations, and source keras models.
This is a companion dataset to the wa-hls4ml dataset. There is a reference CSV for each model type that contains a reference to each individual model name, the artifacts file for that model… See the full description on the dataset page: https://huggingface.co/datasets/fastmachinelearning/wa-hls4ml-projects.PersonaMem-v1🚨 We have now released PersonaMem-v3 and PersonaMem-v2.
This is the official Huggingface repository of the paper Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale and the PersonaMem benchmark.
We present PersonaMem, a new LLM personalization benchmark to assess how well language models can infer evolving user profiles and generate personalized responses across task scenarios. PersonaMem emphasizes persona-oriented, multi-session… See the full description on the dataset page: https://huggingface.co/datasets/bowen-upenn/PersonaMem-v1.gpqa
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.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.PersonaMem-v3
PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Tasks
Bowen Jiang, Yuan Yuan, Zhuoqun Hao, Yuchen Liu, Maohao Shen, Sihao Chen, Gregory Wornell,
Chris Callison-Burch, Lyle Ungar, Dan Roth, Qi Guo, Xiangjun Fan, Camillo J. Taylor, Hanchao Yu
A collaboration between:
Meta Recommendation Systems
University of Pennsylvania
MIT
Third release in the PersonaMem series:
PersonaMem-v1: [COLM… See the full description on the dataset page: https://huggingface.co/datasets/bowen-upenn/PersonaMem-v3.panda-bench
PandaBench
PandaBench is a comprehensive benchmark for evaluating Large Language Model (LLM) safety, focusing on jailbreak attacks, defense mechanisms, and evaluation methodologies.
The PandaGuard framework architecture illustrating the end-to-end pipeline for LLM safety evaluation. The system connects three key components: Attackers, Defenders, and Judges.
Dataset Description
This repository contains the benchmark results from extensive evaluations of various… See the full description on the dataset page: https://huggingface.co/datasets/Beijing-AISI/panda-bench.taxbench-au
TaxBench-AU
A benchmark for testing whether AI agents can calculate Australian tax.
TaxBench-AU contains 156 Australian tax calculation questions, presented as multiple-choice (4-option) worked tax problems. The benchmark is designed to test whether an AI agent can read the facts, apply the right Australian tax rule for the relevant income year, do the calculation, and choose the correct answer.
The Kaggle mirror is published as Agent Tax Exam for Australian Tax.
Paper:… See the full description on the dataset page: https://huggingface.co/datasets/Pn101/taxbench-au.discoverybenchData-driven Discovery Benchmark from the paper:
"DiscoveryBench: Towards Data-Driven Discovery with Large Language Models"
🔭 Overview
DiscoveryBench is designed to systematically assess current model capabilities in data-driven discovery tasks and provide a useful resource for improving them. Each DiscoveryBench task consists of a goal and dataset(s). Solving the task requires both statistical analysis and semantic reasoning. A faceted evaluation allows open-ended… See the full description on the dataset page: https://huggingface.co/datasets/allenai/discoverybench.