datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Chinese_Debate_Documents
Dataset Card for Chinese Debate Documents
ASR-transcribed corpus of competitive Mandarin Chinese university debates,
speaker-segmented and timestamped, with topic / round / team metadata parsed
from the source filenames.
Loading the Dataset
from datasets import load_dataset
ds = load_dataset("DBWBD/Chinese_Debate_Documents", split="train")
print(ds[0]["topic"], "—", ds[0]["team_a"], "vs", ds[0]["team_b"])
for seg in ds[0]["segments"][:3]:
print(f"… See the full description on the dataset page: https://huggingface.co/datasets/DBWBD/Chinese_Debate_Documents.DEBATE
DEBATE: Diverse Multi-Agent Debates
This dataset is presented in the paper "MALLM: Multi-Agent Large Language Models Framework".
Citation
comming soon.
deepa2
deepa2 Datasets Collection
Dataset Summary
This is a growing, curated collection of deepa2 datasets, i.e. datasets that contain comprehensive logical analyses of argumentative texts. The collection comprises:
datasets that are built from existing NLP datasets by means of the deepa2 bake tool.
original deepa2 datasets specifically created for this collection.
The tool deepa2 serve may be used to render the data in this collection as text2text examples.… See the full description on the dataset page: https://huggingface.co/datasets/DebateLabKIT/deepa2.DEBATE_LLM
DEBATE Benchmark
This repository contains CSV files from the DEBATE project: large-scale
human conversation experiments organized around controversial and
opinion-based topics. The data consists of multi-round conversations
between human participants discussing political, social, and belief-related
topics, following the protocol described in:
Chuang, Y.-S., Tu, R., Dai, C., Vasani, S., Li, Y., Yao, B., Tessler, M. H., Yang, S., Shah, D., Hawkins, R., Hu, J., & Rogers, T. T. (2026).… See the full description on the dataset page: https://huggingface.co/datasets/seantw/DEBATE_LLM.DebateSum
DebateSum
Corresponding code repo for the upcoming paper at ARGMIN 2020: "DebateSum: A large-scale argument mining and summarization dataset"
Arxiv pre-print available here: https://arxiv.org/abs/2011.07251
Check out the presentation date and time here: https://argmining2020.i3s.unice.fr/node/9
Full paper as presented by the ACL is here: https://www.aclweb.org/anthology/2020.argmining-1.1/
Video of presentation at COLING 2020:… See the full description on the dataset page: https://huggingface.co/datasets/Hellisotherpeople/DebateSum.deepa2-conversations
Summary
This dataset contains multi-turn conversations that gradually unfold deep logical analyses of argumentative texts.
In particular, the chats contain examples of how to
use Argdown syntax
logically formalize arguments in FOL (latex, nltk etc.)
annotate an argumentative text
use Z3 theorem prover to check deductive validity
use custom tools in conjunction with argument reconstructions
The chats are template-based renderings of the synthetic, comprehensive argument analyses… See the full description on the dataset page: https://huggingface.co/datasets/DebateLabKIT/deepa2-conversations.argunauts-thinkingCreated with argdown-cotgen
deep-argmap-conversations
Summary
This converstional dataset contains examples for how to create and work with Argdown argument maps.
The following tasks are covered:
Create an argument map from a list of statements
Create an argument map from a pros and cons list
Add claims / arguments to an existing argument map
Correct and revise a broken argument map
Merge several argument maps into a single comprehensive one
Identify and add premises / conclusions to an argument map
Reconstruct an argument from a map… See the full description on the dataset page: https://huggingface.co/datasets/DebateLabKIT/deep-argmap-conversations.english-debate-motions-utdsEnglish Debate Motions gathered by University of Tokyo Debate Society
@misc{english-debate-motions-utds,
title={english-debate-motions-utds},
author={members of the University of Tokyo Debate Society},
year={2022},
}
AceReason-1.1-SFT-500kFirst 500k rows from NVIDIA's nvidia/AceReason-1.1-SFT.
arguments-and-debates
Arguments & Debates
Carefully chosen argumentative texts suitable for exercising argument mapping and logical analysis.
Chosen and postprocessed from great sources that can be accessed online:
different editions of "Pros and Cons - A Debater's Handbook"
Britannica's procon.org
NYT column "Room for Debate" via I. Habernal's ARC task repo 🙏
Debatabase by idebate.net
Subset categories:
arguments-*: one main argument per text
debates-*-handful: 3-5 arguments per text
debates-*-full:… See the full description on the dataset page: https://huggingface.co/datasets/DebateLabKIT/arguments-and-debates.debate-tracking-v3
Debate Tracking Dataset v3
Training data from 30 competitive debates (10 topics × 3 judges) with multi-response scoring.
Dataset Description
Each row represents a single LLM call during debate generation, with multiple response variations scored by Claude Sonnet.
Statistics
Debates: 30
Topics: 10 diverse IPDA debate resolutions
Judges: 3 different judge profiles (lay, parent, coach)
Training Examples: 1,816 calls
Winner Distribution: AFF 33%, NEG 67%… See the full description on the dataset page: https://huggingface.co/datasets/debaterhub/debate-tracking-v3.steuer_debateThe Steuer Debate consultation is a german citizen participation project on fair taxes and finances held in 2025.
Data
This dataset contains three subsets:
proposals contains the written propositions (in german). the topic column contains the LLM-generated and human-validated topics (clusters) used during the official analysis of the consultation. Each proposal has a unique id proposal_id.
votes contains the votes of users on propositions. Each user has a unique id user_id and… See the full description on the dataset page: https://huggingface.co/datasets/democratic-commons/steuer_debate.deepa2-corpusomnimcp_multiagent_debate_consensus_teaser
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📜 Enterprise Compliance: EU AI Act Articles 50 & 53 certified • 100% DSGVO / GDPR clean • Commercial EULA… See the full description on the dataset page: https://huggingface.co/datasets/emgena/omnimcp_multiagent_debate_consensus_teaser.argunauts-thinking-202601
DebateLabKIT / argunauts-thinking-202601
This dataset contains aligned variants of the original DebateLabKIT/argunauts-thinking configs:
deepa2-aaac01-thinking
deepa2-aaac02-thinking
deepa2-aaac03-thinking
deepa2-folly-thinking
The goal is to improve the alignment between user prompts and assistant responses in synthetic argumentation conversations for supervised fine-tuning (SFT).
Motivation
The original argunauts-thinking conversations often exhibit a mismatch:… See the full description on the dataset page: https://huggingface.co/datasets/DebateLabKIT/argunauts-thinking-202601.debategpt
Dataset Card for DebateGPT
The DebateGPT dataset contains debates between humans and GPT-4, along with sociodemographic information about human participants and their agreement scores before and after the debates.
This dataset was created for research on measuring the persuasiveness of language models and the impact of personalization, as described in this paper: On the Conversational Persuasiveness of GPT-4.
Dataset Details
The dataset consists of a CSV file with the… See the full description on the dataset page: https://huggingface.co/datasets/frasalvi/debategpt.debatesIndia-Lok-Sabha-Debates-Dataset-Scrapernemo-codeswitch-reasoning-debate
Overview
This is a synthetic, multilingual code-switching dataset. Each record contains:
a realistic user query
a long-form reasoning section
a debate / counterargument section
a concise final_answer
It is designed for experiments in multilingual generation, code-switch robustness, and reasoning/debate style responses.
This snapshot contains 574,977 rows and 10 string columns.
Data provenance
Important:
Verify that your intended usage and redistribution complies… See the full description on the dataset page: https://huggingface.co/datasets/lxyuan/nemo-codeswitch-reasoning-debate.IBM-Debater-ArgKPeu_debates
Dataset Description
This dataset is a conversion of the original coastalcph/eu_debates dataset released by Chalkidis and Brandl (2024).
The goal of this repository is to provide the same underlying data without a Python loading script, in a standard format (JSON Lines / Parquet) compatible with the current Hugging Face datasets library and automated data loading.
The original EU Debates corpus consists of approx. 87k individual speeches in the period 2009–2023.
The data was… See the full description on the dataset page: https://huggingface.co/datasets/RJuro/eu_debates.debate_speeches
Debate speeches dataset
A dataset of annotated debate speeches on various topics. The data contains speeches by human expert debaters as well as speeches created using automated pipelines. The quality of the speeches is scored by human annotators.
Opening Speeches
This is a collection of annotated opening speeches, as described in the Project Debater paper published in Nature. A detailed description of the data collection process can be found here.
Each row in the dataset… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/debate_speeches.argument_mapping_dpo_pairscongreso-debates
Spanish Congress of Deputies: Debates, Verbatim Speeches, and Voting Records (L1 to L15)
High-fidelity dataset containing the parliamentary archive of the Spanish Congress of Deputies (Congreso de los Diputados de España) from the Constituent Legislature / L1 (1979) to the present day (L15).
It includes 41,125 parliamentary interventions with over 188.6 million characters of verbatim speech text extracted across all 1,028 official Daily of Sessions (Diario de Sesiones) PDFs… See the full description on the dataset page: https://huggingface.co/datasets/hsilvosa/congreso-debates.ipda-judge-adaptation-grpo
IPDA Judge Adaptation GRPO Dataset
Training data for judge adaptation in competitive debate. Contains GRPO preference sets for adapting debate speech generation to different judge profiles.
Dataset Description
This dataset enables training LLMs to adapt their debate arguments based on judge characteristics:
Depth Adaptation: Adapting explanation complexity to judge expertise level (debate experience + domain knowledge)
Bias Adaptation: Adapting argument framing to judge… See the full description on the dataset page: https://huggingface.co/datasets/debaterhub/ipda-judge-adaptation-grpo.argdown_line-by-linedebate-argumentation-sft-100k
Debate & Argumentation SFT (100K)
100,000 ShareGPT conversations covering persuasive writing, steel-manning, rebuttal, policy analysis, and Socratic dialogue. Each example trains models to construct well-structured arguments, anticipate counterarguments, and engage in rigorous intellectual discourse.
Motivation
A persistent gap in LLM capabilities is the ability to reason and argue well — not just describe positions, but construct arguments with premises, evidence… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/debate-argumentation-sft-100k.debateorg_w_effect_for_conservative_subset
Dataset Card for "debateorg_w_effect_for_conservative_subset"
More Information needed
debate-grpo-group-a
Debate GRPO Group A - TACTIC_SELECT
Training data for debate model GRPO fine-tuning (Group A: TACTIC_SELECT calls).
Files
File
Description
Rows
group_a_rescored_v2_with_logps.parquet
Training format (one row per response) with precomputed logprobs
1,993
group_a_flat_rescored_v2.parquet
Flat format with RESPONSE_1-6 columns per call
520
Training Format Columns
Column
Description
debate_id
Unique debate identifier
call_id… See the full description on the dataset page: https://huggingface.co/datasets/debaterhub/debate-grpo-group-a.