datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
BrowseCompMultiTurn-Chat-MT-Bench-Judge
SEA-MT-Bench-Judge
SEA-MT-Bench-Judge expands on the original SEA-MTBench through the use of a criteria-based evaluation framework. We use GPT-OSS-120B as the judge model.
The prompts are based on MT-Bench and was manually translated by native speakers. Furthermore, some prompts were modified to be more suitable for the criteria-based judgments.
Supported Tasks and Leaderboards
SEA-MT-Bench-Judge is designed for evaluating chat or instruction-tuned large language… See the full description on the dataset page: https://huggingface.co/datasets/aisingapore/MultiTurn-Chat-MT-Bench-Judge.deepfabric-7k-medical-multi-turn-conversation
Medical Education Curriculum Dataset by Deepfabric
Dataset Description
This synthetic dataset contains 7,570 high-quality conversations focused on medical education curriculum design
and clinical training. The conversations simulate realistic discussions between medical curriculum
committee chairs, educators, and healthcare professionals designing comprehensive learning pathways.
It was produced using the Open Source Synthetic dataset generation tool, DeepFabric… See the full description on the dataset page: https://huggingface.co/datasets/nolabs/deepfabric-7k-medical-multi-turn-conversation.IFBench_multi-turn
Dataset
This is the test data for the multi-turn setup of IFBench.
License
This dataset is licensed under ODC-BY-1.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines. This dataset includes output data generated from third party models that are subject to separate terms governing their use.
Citation
Please cite:
@misc{pyatkin2025generalizing,
title={Generalizing Verifiable Instruction Following}… See the full description on the dataset page: https://huggingface.co/datasets/allenai/IFBench_multi-turn.spoken-multiturn-sft
Spoken Multi-turn SFT Japanese
Japanese spoken multi-turn SFT dataset generated from kanhatakeyama/AutoMultiTurnByCalm3-22B using CosyVoice2 TTS.
Dataset Description
This dataset contains Japanese multi-turn SFT (Supervised Fine-Tuning) data with spoken questions.
q1: First question (text + audio)
a1: First answer (text only)
q2: Follow-up question (text + audio)
a2: Second answer (text only)
Samples
ID
Q1
Q1 Audio
A1
Q2
Q2 Audio
A2
0
鉄は強磁性体ですか？… See the full description on the dataset page: https://huggingface.co/datasets/Atotti/spoken-multiturn-sft.ultrachat_speech_multiTurnstool-use-multiturn-reasoningDolci-Think-SFT-7B-multiturnQwen3.8-27B-multi-turn-agent-sft
Qwen3.8-multi-turn-agent-sft
Hello everyone! We are UkisAI, a small research lab from Europe.
We created this dataset based on the OpenThoughts-Agent-v1-SFT dataset. The traces in this release were generated with Qwen3.8-27B in FP16 using the Terminus-2 agentic harness.
This dataset contains approximately 15,200 agent traces covering terminal, coding, and software-engineering tasks, including tasks from nl2bash and InferredBugs.
Please feel free to try it, share feedback, report… See the full description on the dataset page: https://huggingface.co/datasets/ukisai/Qwen3.8-27B-multi-turn-agent-sft.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.sql-multiturn-training-dataset-combinedknowchat-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.bfcl_v3_multi_turn_basewildchat-en-multiturnmultiturn_ks
khursanirevo/multiturn_ks
Dataset Description
Multiturn dialogue dataset with speaker-separated stereo audio and multi-language transcripts from 139 YouTube videos.
Features
Audio: Stereo audio with speaker separation (speaker 0 = left channel, speaker 1 = right channel)
Segments: Speaker turn-level annotations with timestamps for English and Malay
Multi-language: Transcripts in 9 languages (en, ms, zh-Hans, zh-Hant, ru, id, ar, ja, ko)
Video ID: YouTube video… See the full description on the dataset page: https://huggingface.co/datasets/khursanirevo/multiturn_ks.tool-calls-multiturnmultiturn_processedSWE-Gym-Smallolmo-3-preference-mix-deltas_reasoning-yolo_scottmix-DECON-multi-turntraining-datasetcrag-mm-multi-turn-public
CRAG-MM: Comprehensive multi-modal, multi-turn RAG Benchmark
This repository contains the CRAG-MM dataset, a high-quality conversational benchmark for multimodal assistants. The dataset features conversations about images with varied complexity levels, designed to evaluate AI systems' visual understanding and conversational abilities.
CRAG-MM is a visual question-answering benchmark that focuses on factual questions, offering a unique collection of image and question-answering sets… See the full description on the dataset page: https://huggingface.co/datasets/crag-mm-2025/crag-mm-multi-turn-public.craft-multiturn-actions-split-nothinkSafety_Reasoning_Multi_Turn_Dialogue
Paper and Citation
More technical details can be found in our paper. If you find Safety_Reasoning_Multi_Turn_Dialogue useful or relevant to your project and research, please kindly cite our paper:
@article{kuo2025safety,
title={SafeTy Reasoning Elicitation Alignment for Multi-Turn Dialogues},
author={Kuo, Martin and Zhang, Jianyi and Ding, Aolin and DiValentin, Louis and Hass, Amin and Morris, Benjamin F and Jacobson, Isaac and Linderman, Randolph and Kiessling, James and Ramos… See the full description on the dataset page: https://huggingface.co/datasets/DukeCEICenter/Safety_Reasoning_Multi_Turn_Dialogue.prm800k_onpolicy_multiturn_rtg_prefix0.2_roll4_maxrev100multiturn_chat_0.8m-chinese-zhtw
Dataset Card for "multiturn_chat_0.8m-chinese-zhtw"
內容
包含約 80 萬條由 BELLE 專案所產生的 user 與 assistant 的多輪對話。
注意：此資料集是由 ChatGPT 產生的，未經嚴格校驗，內容可能包含錯誤。使用過程中請注意這一點。
限制和使用限制
我們要求開發者僅將我們開源的程式碼、資料、模型及後續衍生物用於研究目的，不得用於商業，以及其他會對社會帶來危害的用途。
由於數據是由ChatGPT產生的，未經嚴格驗證，在事實性和其他方面仍有一些不足之處。因此，在使用此資料集時，請務必注意甄別。
本資料集不代表任何一方的立場、利益或想法，無關任何團體的任何類型的主張。因使用本資料集帶來的任何損害、糾紛，本專案的開發者不承擔任何責任。
Multiturn Chat 0.8M
Contents
Includes approx. 0.8M Chinese multiturn dialogs between… See the full description on the dataset page: https://huggingface.co/datasets/benchang1110/multiturn_chat_0.8m-chinese-zhtw.text2CAD-multiturn-reasoningMalaysian-Multiturn-Chat-Assistant
Malaysian-Multiturn-Chat-Assistant
Generate synthetic multi-turn chat assistant with complex system prompt using mesolitica/Malaysian-Qwen2.5-72B-Instruct.
After that generate synthetic voice using mesolitica/Malaysian-Dia-1.6B also verified with Force Alignment to make sure the pronunciations almost correct.
A conversation must at least have 2 audio. We follow chat template from Qwen/Qwen2-Audio-7B-Instruct.
how to prepare the dataset
huggingface-cli download \… See the full description on the dataset page: https://huggingface.co/datasets/mesolitica/Malaysian-Multiturn-Chat-Assistant.multi_turn_function_callingscanqa_images_64_336x224_672x448_multiturnmultiturn-512-UltraInteract_pair_diff_len