datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
commonsense_qa
Dataset Card for "commonsense_qa"
Dataset Summary
CommonsenseQA is a new multiple-choice question answering dataset that requires different types of commonsense knowledge
to predict the correct answers . It contains 12,102 questions with one correct answer and four distractor answers.
The dataset is provided in two major training/validation/testing set splits: "Random split" which is the main evaluation
split, and "Question token split", see paper for details.… See the full description on the dataset page: https://huggingface.co/datasets/tau/commonsense_qa.truthful_qa
Dataset Card for truthful_qa
Dataset Summary
TruthfulQA is a benchmark to measure whether a language model is truthful in generating answers to questions. The benchmark comprises 817 questions that span 38 categories, including health, law, finance and politics. Questions are crafted so that some humans would answer falsely due to a false belief or misconception. To perform well, models must avoid generating false answers learned from imitating human texts.… See the full description on the dataset page: https://huggingface.co/datasets/truthfulqa/truthful_qa.trivia_qa
Dataset Card for "trivia_qa"
Dataset Summary
TriviaqQA is a reading comprehension dataset containing over 650K
question-answer-evidence triples. TriviaqQA includes 95K question-answer
pairs authored by trivia enthusiasts and independently gathered evidence
documents, six per question on average, that provide high quality distant
supervision for answering the questions.
Supported Tasks and Leaderboards
More Information Needed
Languages… See the full description on the dataset page: https://huggingface.co/datasets/mandarjoshi/trivia_qa.qasc
Dataset Card for "qasc"
Dataset Summary
QASC is a question-answering dataset with a focus on sentence composition. It consists of 9,980 8-way multiple-choice
questions about grade school science (8,134 train, 926 dev, 920 test), and comes with a corpus of 17M sentences.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
default
Size of… See the full description on the dataset page: https://huggingface.co/datasets/allenai/qasc.hotpot_qa
Dataset Card for "hotpot_qa"
Dataset Summary
HotpotQA is a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowingQA systems to reason… See the full description on the dataset page: https://huggingface.co/datasets/hotpotqa/hotpot_qa.cosmos_qacommonsense-qaqascmed_qa
Dataset Card for MedQA
In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA,
collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and
traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together
with the question data, we also collect and release a large-scale corpus from medical textbooks from which the… See the full description on the dataset page: https://huggingface.co/datasets/bigbio/med_qa.Legal_Corpus_QA_SynDeepThink
🧠 Legal Corpus QA SynDeepThink Dataset
This repository contains a high-intelligence Legal Question-and-Answer dataset, generated through an advanced Iterative and Recursive Thinking process. It bridges the gap between static legal corpora and the dynamic "check-and-recheck" nature of human legal expertise. 🏛️
💡 The Concept: Iterative & Recursive Legal Logic
While standard synthetic datasets are often generated in a single pass, Legal_Corpus_QA_SynDeepThink mimics the… See the full description on the dataset page: https://huggingface.co/datasets/Azzindani/Legal_Corpus_QA_SynDeepThink.medical-qa-datasets
all-processed dataset is a concatenation of of medical-meadow-* and chatdoctor_healthcaremagic datasets
The Chat Doctor term is replaced by the chatbot term in the chatdoctor_healthcaremagic dataset
Similar to the literature the medical_meadow_cord19 dataset is subsampled to 50,000 samples
truthful-qa-* is a benchmark dataset for evaluating the truthfulness of models in text generation, which is used in Llama 2 paper. Within this dataset, there are 55 and 16 questions related to Health and… See the full description on the dataset page: https://huggingface.co/datasets/lavita/medical-qa-datasets.qa4mre
Dataset Card for "qa4mre"
Dataset Summary
QA4MRE dataset was created for the CLEF 2011/2012/2013 shared tasks to promote research in
question answering and reading comprehension. The dataset contains a supporting
passage and a set of questions corresponding to the passage. Multiple options
for answers are provided for each question, of which only one is correct. The
training and test datasets are available for the main track.
Additional gold standard documents are… See the full description on the dataset page: https://huggingface.co/datasets/community-datasets/qa4mre.vsi-bench-qa-v3-hm3d-fulladversarial_qa
Dataset Card for adversarialQA
Dataset Summary
We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2018), and RoBERTaLarge (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples.
The adversarial human… See the full description on the dataset page: https://huggingface.co/datasets/UCLNLP/adversarial_qa.wiki_qa
Dataset Card for "wiki_qa"
Dataset Summary
Wiki Question Answering corpus from Microsoft.
The WikiQA corpus is a publicly available set of question and sentence pairs, collected and annotated for research on open-domain question answering.
Supported Tasks and Leaderboards
More Information Needed
Languages
More Information Needed
Dataset Structure
Data Instances
default
Size of downloaded dataset files: 7.10 MB
Size… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/wiki_qa.qasperA dataset containing 1585 papers with 5049 information-seeking questions asked by regular readers of NLP papers, and answered by a separate set of NLP practitioners.strategy-qaKnowledge-QA-SingleTurn-Dataset
Knowledge QA Single-turn Dataset（知識質問データセット・シングルターン）
概要
本データセットは、Aratako/Synthetic-JP-Conversations-Magpie-Nemotron-4-10k から質問を抽出し、DeepSeek V3.2で整形、Kimi K2.5で回答を生成した シングルターンの知識質問応答データセット です。Reasoning有効化により思考過程も最終データに含まれ、質問の難易度に応じてReasoning effortが動的に切り替わります。
生成にはSDG-LOOMという合成データ生成パイプラインを用いました。（sdg-loom）
データの説明
項目
内容
件数
約7,000件
形式
JSONL（1行1JSON）
言語
日本語
ターン数
1ターン（質問1 + 回答1）
ソースデータセット… See the full description on the dataset page: https://huggingface.co/datasets/DataPilot/Knowledge-QA-SingleTurn-Dataset.cybersecurity-qa-v2
Cybersecurity Q&A Dataset v2 — 2.6M Examples
A large-scale cybersecurity Q&A dataset for fine-tuning LLMs on security topics.
2,621,468 examples covering vulnerabilities, attack techniques, weaknesses, and defensive strategies.
Statistics
Source
Examples
Description
NIST NVD CVE Database
~1,954,225
All CVEs (2002–2025): overview, severity, detection, remediation
AlicanKiraz0/All-CVE-Records-Training-Dataset
~297,441
Detailed CVE analysis with markdown… See the full description on the dataset page: https://huggingface.co/datasets/rezaduty/cybersecurity-qa-v2.vsi-bench-qa-v3-hm3d-1k-testhabitat-perspective-qa-train-v2
Habitat HM3D Perspective Taking QA - Train v2
context_qa_sum_qwen3_synthetic
Context-based QA and Summarization Synthetic Dataset
Overview
This dataset contains synthetic context-based question-answering (QA) and summarization data. The data was synthesized using:
Source context: openbmb/Ultra-FineWeb
Synthesis model: Qwen3-30B-A3B-Instruct-2507
Each context is obtained by taking the initial segment of raw pretraining text from Ultra-FineWeb, truncated to at most the corresponding number of tokens, while ensuring the truncation does not occur in… See the full description on the dataset page: https://huggingface.co/datasets/yuyijiong/context_qa_sum_qwen3_synthetic.ID_Legal_QA_SynDeepThink
🧠 Indonesian Legal QA SynDeepThink Dataset
This repository hosts a specialized Indonesian Legal QA dataset that incorporates a Deep Thinking Phase. It is engineered for researchers and developers focusing on high-level judicial reasoning and complex regulatory analysis. 🏛️
💡 The Concept: Deep Thinking vs. Standard QA
While standard models often provide "System 1" (snap) judgments, the SynDeepThink approach simulates "System 2" (slow, deliberate) thinking. This dataset… See the full description on the dataset page: https://huggingface.co/datasets/Azzindani/ID_Legal_QA_SynDeepThink.QAEgo4D-MC-testThis benchmark was collected by QAEgo4D and updated by GroundVQA.
We conducted some processing for the experiments presented in our paper ReKV.
ledger-long-context-KPI-QA
LEDGER — Long-Context KPI Question Answering & Page Retrieval
This dataset is part of the LEDGER (Long-context Evaluation of Documents for
Grounded Extraction and Retrieval) benchmark.
It supports two of the three LEDGER tasks:
Page-level KPI retrieval — given a natural-language question about a financial
KPI and the corresponding annual report, retrieve the relevant page(s). Each row
includes TREC-style graded relevance judgments (qrels) over all candidate pages.… See the full description on the dataset page: https://huggingface.co/datasets/artefactory/ledger-long-context-KPI-QA.enron-qa-emails-dasovich-jmultispeech_qa
MultispeechQA Dataset
Multilingual spoken-audio question-answering dataset covering 16 languages: Arabic, Czech, German, Greek, French, Hebrew, Hindi, Indonesian, Japanese, Korean, Dutch, Portuguese, Romanian, Spanish, Turkish, Ukrainian. Each example pairs an audio clip with a question and its answer.
Each language config has train / test / validation splits, sharded as multiple .parquet files.
from datasets import load_dataset
ds = load_dataset("your-username/multispeech-qa"… See the full description on the dataset page: https://huggingface.co/datasets/tolulope/multispeech_qa.lsat_qaqa_squadshifts_synthetic
Dataset Card for "lmqg/qa_squadshifts_synthetic"
Dataset Summary
This is a synthetic QA dataset generated with fine-tuned QG models over lmqg/qa_squadshifts, made for question-answering based evaluation (QAE) for question generation model proposed by Zhang and Bansal, 2019.
The test split is the original validation set of lmqg/qa_squadshifts, where the model should be evaluate on.
Supported Tasks and Leaderboards
question-answering
Languages… See the full description on the dataset page: https://huggingface.co/datasets/lmqg/qa_squadshifts_synthetic.amnesty_qa
Amnesty QA Dataset
A grounded question-answering dataset for evaluating RAG (Retrieval-Augmented Generation) systems, created from reports collected from Amnesty International.
This dataset is designed for testing and evaluating RAG pipelines with real-world human rights content.
Dataset Structure
Each sample contains:
user_input: The question to be answered
reference: Ground truth answer for evaluation
response: Generated answer from the system
retrieved_contexts: List… See the full description on the dataset page: https://huggingface.co/datasets/by254126/amnesty_qa.