datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
MultiHopRAG
Dataset Card for Dataset Name
A Dataset for Evaluating Retrieval-Augmented Generation Across Documents
Dataset Description
MultiHop-RAG: a QA dataset to evaluate retrieval and reasoning across documents with metadata in the RAG pipelines. It contains 2556 queries, with evidence for each query distributed across 2 to 4 documents. The queries also involve document metadata, reflecting complex scenarios commonly found in real-world RAG applications.
Dataset Sources… See the full description on the dataset page: https://huggingface.co/datasets/yixuantt/MultiHopRAG.CLaRa_multi_stage
CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning
This is the official dataset for the CLaRa paper which contains training and evaluation data for the CLaRa model, organized into three main categories: pretraining, instruction tuning, and end-to-end tuning.
Dataset Structure
1. Pretraining Data
pretraining: Large-scale pretraining data for the compressor learning
Format: JSONL with fields: data_type, question, answers… See the full description on the dataset page: https://huggingface.co/datasets/apple/CLaRa_multi_stage.hle-multilingual
HLE Multilingual
Multilingual translations of HLE (Humanity's Last Exam), an expert-level QA benchmark with questions across math, science, humanities, and engineering designed to challenge even domain experts.
Source: cais/hle (test split, 2,158 text-only questions out of 2,500 total)
Languages
Config
Language
Examples
ces
Czech
50
dan
Danish
50
deu
German
800
fin
Finnish
50
fra
French
50
ita
Italian
50
nld
Dutch
50
pol
Polish
50
spa… See the full description on the dataset page: https://huggingface.co/datasets/ellamind/hle-multilingual.gsm8k-platinum-multilingual
GSM8K Platinum Multilingual
Multilingual translations of GSM8K Platinum, a rigorously cleaned and verified version of GSM8K containing 1,209 elementary math word problems requiring multi-step arithmetic reasoning.
Source: madrylab/gsm8k-platinum (test split, 1,209 questions)
Languages
Config
Language
Examples
ces
Czech
100
dan
Danish
100
deu
German
1,209
fin
Finnish
100
fra
French
100
ita
Italian
100
nld
Dutch
100
pol
Polish
100
spa
Spanish… See the full description on the dataset page: https://huggingface.co/datasets/ellamind/gsm8k-platinum-multilingual.propagator-multimodal-pretraining-data
Propagator Multimodal Pretraining Data
This public dataset contains tokenized multimodal pretraining data prepared for the Propagator model family. It combines language, image-grounded, and speech/audio-token examples into a single training format.
This is not a raw text or image browsing dataset. The examples have already been converted into compact binary token frames for model training, with a manifest that records the source groups and file layout.
Source Code… See the full description on the dataset page: https://huggingface.co/datasets/ken-sungmin/propagator-multimodal-pretraining-data.tiny-aya-l2-thinker-multilingual-reasoning
Tiny Aya L2 Multilingual Reasoning (44 languages)
Translated multilingual reasoning traces used to train Tiny Aya L2-Thinker.
Each example has the prompt, thinking, and answer in the same non-English language alongside the original texts in English.
Data source
Prompts from AM-DeepSeek-R1-0528-Distilled
Thinking traces and outputs distilled from gpt-oss-120b
Translated with command-a-translate and DeepSeek-V3
Languages (44)
Language
Train… See the full description on the dataset page: https://huggingface.co/datasets/CohereLabs/tiny-aya-l2-thinker-multilingual-reasoning.zoology_multihop
Zoology Multihop Associative Retrieval
irodkin/zoology_multihop is a deterministic synthetic associative-retrieval
dataset with multiple queries per context. It uses integer token arrays rather
than natural-language text.
Configurations
Every configuration is named N{N}-H{H}-V4096 and uses one of:
N: 8, 16, 32, 64, or 128 total key-value edges.
H: 1, 2, 4, or 8 edges per chain.
V: exactly 4,096 total tokens.
The complete release contains all 20 combinations. A… See the full description on the dataset page: https://huggingface.co/datasets/irodkin/zoology_multihop.TableBench
Dataset Card for TableBench
📚 Paper
🏆 Leaderboard
💻 Code
Dataset Summary
TableBench is a comprehensive and complex
benchmark designed to evaluate Table
Question Answering (TableQA) capabilities, aligning closely with the "Reasoning Complexity of
Questions" dimension in real-world Table QA scenarios. It covers 18 question
categories
across 4 major ategories—including… See the full description on the dataset page: https://huggingface.co/datasets/Multilingual-Multimodal-NLP/TableBench.Multi-subject-RLVRMulti-subject data for paper "Expanding RL with Verifiable Rewards Across Diverse Domains".
we use a multi-subject multiple-choice QA dataset ExamQA (Yu et al., 2021).
Originally written in Chinese, ExamQA covers at least 48 first-level subjects.
We remove the distractors and convert each instance into a free-form QA pair.
This dataset consists of 638k college-level instances, with both questions and objective answers written by domain experts for examination purposes.
We also use GPT-4o-mini… See the full description on the dataset page: https://huggingface.co/datasets/virtuoussy/Multi-subject-RLVR.MultiChartQA
MultiChartQA
This repository contains the questions and answers for our Multi-chart Benchmark. At present, only the data is available, but the test code will be provided soon. We welcome everyone to use and explore our benchmark!
Introduction
MultiChartQA is an extensive and demanding benchmark that features real-world charts. We source charts from various places to ensure both diversity and completeness. Each multi-chart group includes 2 or 3 charts, and each group is… See the full description on the dataset page: https://huggingface.co/datasets/Zifeng618/MultiChartQA.multi_lmentry
Multi-LMentry
This dataset card provides documentation for Multi-LMentry, a multilingual benchmark designed for evaluating large language models (LLMs) on fundamental, elementary-level tasks across nine languages. It is the official dataset release accompanying the EMNLP 2025 paper "Multi-LMentry: Can Multilingual LLMs Solve Elementary Tasks Across Languages?".
Dataset Details
Dataset Description
Multi-LMentry is a multilingual extension of LMentry (Efrat et… See the full description on the dataset page: https://huggingface.co/datasets/BSC-LT/multi_lmentry.MultiChallenge
MultiChallenge
MultiChallenge is a benchmark for evaluating large language models on multi-turn conversations. It tests whether models can maintain context, follow instructions, and reason correctly across extended dialogues.
Evaluation Axes
Axis
Description
INFERENCE_MEMORY
Tests whether the model can recall and reason over information from earlier turns
INSTRUCTION_RETENTION
Tests whether the model continues to follow instructions given in earlier turns… See the full description on the dataset page: https://huggingface.co/datasets/ScaleAI/MultiChallenge.multiloko
MultiLoKo: a multilingual local knowledge benchmark for LLMs
MultiLoKo is a multilingual knowledge benchmark, covering 30 languages plus English.
The questions are separately sourced for each language, with an annotation protocol designed to target locally relevant topics for the respective language.
MultiLoKo contains the original data for each language, as well as both human and machine-authored translations of each non-English subset into English and vice versa, facilitating… See the full description on the dataset page: https://huggingface.co/datasets/facebook/multiloko.Traditional-Chinese-Medicine-Multiple_choice_question
Discription
This dataset is sourced from the website of the Ministry of Examination, R.O.C (Taiwan) and contains past exam questions from the national Traditional Chinese Medicine examinations in Taiwan. The exam comprises six subjects. This dataset specifically includes questions from two subjects, including the History of Traditional Chinese Medicine, Basic Theories of Traditional Chinese Medicine, Neijing, Nanjing, Traditional Chinese Medicine Prescription Studies, and… See the full description on the dataset page: https://huggingface.co/datasets/Liavan/Traditional-Chinese-Medicine-Multiple_choice_question.multi-wiki-qa
This dataset is a reading comprehension dataset based on Wikipedia articles coupled with LLM-generated questions and answers.
Dataset Details
Dataset Description
All articles and answers come from Wikipedia articles, and all questions have been generated by Gemini-1.5-pro.
All Wikipedia articles are from this Wikipedia dump, from which we sample randomly with seed 4242.
There is a special case for Mandarin, as the Mandarin Wikipedia mixes Simplified Mandarin with… See the full description on the dataset page: https://huggingface.co/datasets/alexandrainst/multi-wiki-qa.DEBATE
DEBATE: Diverse Multi-Agent Debates
This dataset is presented in the paper "MALLM: Multi-Agent Large Language Models Framework".
Citation
comming soon.
gsm8k-multilingual-reasoning
gsm8k-multilingual-reasoning
GSM8K with reasoning translated to multiple languages
Schema
{"prompt": "...", "answer": "...", "reasoning": "...", "metadata": {...}}
Usage
from datasets importload_dataset
ds = load_dataset("eddie-OB/gsm8k-multilingual-reasoning")
print(ds["train"][0])
Source
Derived from OpenAI GSM8K.
tool-use-multiturn-reasoningTrueFalse-Statements-multilingualThis dataset is introduced in the paper Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through the Lens of Internal Representations.
Code: https://github.com/DAMO-NLP-SG/LLM-Multilingual-Knowledge-Boundaries
Multi-modal-Self-instruct
Dataset Description
Paper Information
Dataset Examples
Leaderboard
Dataset Usage
Data Downloading
Data Format
Evaluation
Citation
You can download the zip dataset directly, and both train and test subsets are collected in Multi-modal-Self-instruct.zip.
Dataset Description
Multi-Modal Self-Instruct dataset utilizes large language models and their code capabilities to synthesize massive abstract images and visual reasoning instructions across daily scenarios. This benchmark… See the full description on the dataset page: https://huggingface.co/datasets/zwq2018/Multi-modal-Self-instruct.Multi-Turn-Insurance-Underwriting
Dataset Card for Multi-Turn-Insurance-Underwriting
Dataset Summary
This dataset includes sample traces and associated metadata from multi-turn interactions between a commercial underwriter and AI assistant. We built the system in langgraph with model context protocol and ReAct agents. In each sample, the underwriter has a specific task to solve related to a recent application for insurance by a small business. We created a diverse sample dataset covering 6 distinct types… See the full description on the dataset page: https://huggingface.co/datasets/snorkelai/Multi-Turn-Insurance-Underwriting.MedQA-Darija-MultiLingual
MedQA-Darija-MultiLingual
The largest open trilingual medical Q&A dataset with directly-playable speech audio for English, French, and Moroccan Darija.
A research dataset for the BRAIN HEALTH initiative, designed for multilingual medical NLP, low-resource speech recognition, healthcare chatbots, and clinical education tools targeting Morocco and the broader Maghreb region.
Dataset is currently in scientific validation phase. After programmatic validation (Stage 1 LOF outlier… See the full description on the dataset page: https://huggingface.co/datasets/Williamsanderson/MedQA-Darija-MultiLingual.gpqa-multilingual
GPQA Multilingual
Multilingual translations of GPQA (Graduate-Level Google-Proof Q&A), a challenging multiple-choice benchmark requiring graduate-level expertise in biology, physics, and chemistry.
Source: Idavidrein/gpqa (gpqa_main, 448 questions)
Languages
Config
Language
Examples
ces
Czech
448
dan
Danish
448
deu
German
448
fin
Finnish
50
fra
French
448
ita
Italian
448
nld
Dutch
448
pol
Polish
448
spa
Spanish
448
More to be added later.… See the full description on the dataset page: https://huggingface.co/datasets/ellamind/gpqa-multilingual.remote_sensing_VQA_multilingual
Remote Sensing VQA — Multilingual
A multilingual counterfactual MCQ dataset built from remote sensing / satellite imagery.
Each row contains a satellite image, two captions (original vs counterfactual), and a multiple-choice question probing whether a VLM follows the image or the misleading text.
Languages
Language
Code
Rows
English
en
50
Hindi
hi
50
Urdu
ur
50
Telugu
te
50
Bahasa Indonesia
id
50
Columns
Column
Type… See the full description on the dataset page: https://huggingface.co/datasets/apart-global-south-hack/remote_sensing_VQA_multilingual.knowchat-multi-turn-dialogues
KnowChat: Multi-Turn Human-LLM Dialogues on Knowledge Tasks
KnowChat is a dataset of 705 multi-turn human-LLM conversations collected to validate the KnowSim user simulation framework. It pairs each conversation with pre/post knowledge assessments, self-reported survey ratings, and participant background information, enabling research on information calibration -- how well LLM assistants tailor responses to users with different knowledge levels.
Dataset Summary… See the full description on the dataset page: https://huggingface.co/datasets/yjlee36/knowchat-multi-turn-dialogues.Kairos-Multimodal-Reasoning
A dataset for training models in multimodal reasoning tasks
Usage
from datasets import load_dataset
ds = load_dataset("Aquiles-ai/Kairos-Multimodal-Reasoning")
print(ds.features)
print(ds["train"]["source"])
Preview of dataset examples
We've built a playground so you can see some of the examples included in the dataset.
Link: https://kairos-example.vercel.app/
Dataset used in the blog post: Kairos: Building a Multimodal Model with LFM2.5 and… See the full description on the dataset page: https://huggingface.co/datasets/Aquiles-ai/Kairos-Multimodal-Reasoning.Multi-turn_Long-context_Benchmark_for_LLMs
LoopServe: An Adaptive Dual-phase LLM Inference Acceleration System for Multi-Turn Dialogues
Arxiv: https://www.arxiv.org/abs/2507.13681
Huggingface: https://huggingface.co/papers/2507.13681
Introduction
LoopServe Multi-Turn Dialogue Benchmark is a comprehensive evaluation dataset comprising multiple diverse datasets designed to assess large language model performance in realistic conversational scenarios.
Unlike traditional benchmarks that place queries only at the end… See the full description on the dataset page: https://huggingface.co/datasets/TreeAILab/Multi-turn_Long-context_Benchmark_for_LLMs.multidoc2dialMultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents.gsm8k-multilingual
gsm8k-multilingual
GSM8K translated to multiple languages (no reasoning)
Schema
{"prompt": "...", "answer": "...", "metadata": {...}}
Usage
from datasets import load_dataset
ds = load_dataset("eddie-OB/gsm8k-multilingual")
print(ds["train"][0])
Source
Derived from OpenAI GSM8K.
simpleqa-verified-multilingual
SimpleQA Verified Multilingual
Multilingual translations of SimpleQA Verified, a 1,000-prompt factuality benchmark from Google DeepMind that evaluates short-form parametric knowledge (facts stored in model weights).
Source: google/simpleqa-verified (eval split, 1,000 examples)
Languages
Config
Language
Examples
ces
Czech
100
dan
Danish
100
deu
German
1,000
fra
French
100
ita
Italian
100
nld
Dutch
100
pol
Polish
100
spa
Spanish
100
More to… See the full description on the dataset page: https://huggingface.co/datasets/ellamind/simpleqa-verified-multilingual.