Datasets:

seantw
/

DEBATE_LLM

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 participants discussing political, social, and belief-related topics, following the protocol described in:

Yun-Shiuan Chuang, Ruixuan Tu, Chengtao Dai, Smit Vasani, You Li, Binwei Yao, Michael Henry Tessler, Sijia Yang, Dhavan Shah, Robert Hawkins, Junjie Hu, & Timothy T. Rogers. (2026). DEBATE: A large-scale benchmark for evaluating opinion dynamics in role-playing LLM agents (arXiv:2510.25110) [Preprint]

Paper Link: https://arxiv.org/abs/2510.25110

Directory Structure

.
├── raw/                       # Raw exports
│   ├── depth/                 # Topic Set 1: Depth topics (fewer topics, more conversations each)
│   │   ├── [topic_name]/
│   │   │   ├── *.csv
│   │   │   └── ...
│   │   └── ...
│   └── breadth/               # Topic Set 2: Breadth topics (many topics, fewer conversations each)
│       ├── [topic_name]/
│       │   ├── *.csv
│       │   └── ...
│       └── ...
├── golden/                    # Curated golden subsets
│   ├── depth/
│   └── breadth/
├── README.md
└── VERSION_LOG.md

Data Organization

Depth Topic Set vs Breadth Topic Set

Depth Topics (7 Topics): Focused exploration of a smaller set of topics with multiple conversation sessions per topic
Breadth Topics (100 Topics): Broad coverage across many different topics with fewer sessions per topic
For more information on topics, check Appendix.

File Naming Convention

Each CSV file follows this naming pattern:

YYYYMMDD_HHMMSS_TOPIC_NAME_UNIQUE_ID.csv

Where:

YYYYMMDD: Date (Year/Month/Day)
HHMMSS: Time (Hour/Minute/Second)
TOPIC_NAME: Underscored topic description
UNIQUE_ID: 26-character unique identifier

Data File Structure

Each CSV file contains conversation data with the following key columns:

Event tracking: event_order, event_type
Participants: worker_id, sender_id, recipient_id
Content: text (messages, opinions, slider values)
Conversation flow: chat_round_order, message_id
User interaction: is_slider_changed (opinion rating changes)

Event Types

Initial Opinion: Participant's starting position on the topic
tweet: Short messages during conversation
message_sent/message_received: Direct messages between participants

Special Notation

[SLIDER_VALUE=X]: Indicates participant's opinion rating (typically 1-5 scale)
[AUTOSUBMISSION DUE TO TIME LIMIT]: System-generated due to timeout

Data Usage

This dataset is suitable for research on:

Opinion dynamics and persuasion
Human-AI conversation patterns
Political and social belief systems
Argumentation and debate analysis
Consensus building in controversial topics

Data Quality

Files contain real human conversation data
Some conversations may be incomplete due to participant dropout
Time limits may have caused automatic submissions
Processed data may contain empty rows: Consecutive messages from the same user are concatenated and treated as a single message, which can result in empty rows in the processed dataset

License & Usage Restrictions

This dataset is released under the:

DEBATE Dataset Research-Only License (Non-Commercial, v1.0) (see the LICENSE file in this repository).

Citation

Please cite the following work when using this dataset in your research:

@article{chuang2025debate,
  title   = {DEBATE: A Large-Scale Benchmark for Evaluating Opinion Dynamics in Role-Playing LLM Agents},
  author  = {Chuang, Yun-Shiuan and Tu, Ruixuan and Dai, Chengtao and Vasani, Smit and Li, You and Yao, Binwei and Tessler, Michael Henry and Yang, Sijia and Shah, Dhavan and Hawkins, Robert and Hu, Junjie and Rogers, Timothy T.},
  year    = {2025},
  journal = {arXiv preprint arXiv:2510.25110},
  doi     = {10.48550/arXiv.2510.25110},
  url     = {https://arxiv.org/abs/2510.25110}
}