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+ ],
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+ "name": "DEBATE_LLM",
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+ "description": "\n\t\n\t\t\n\t\tDEBATE Benchmark\n\t\n\nThis repository contains CSV files from the DEBATE project: large-scale\nhuman conversation experiments organized around controversial and\nopinion-based topics. The data consists of multi-round conversations\nbetween human participants discussing political, social, and belief-related\ntopics, following the protocol described in:\n\nChuang, 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.",
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+ "alternateName": [
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+ "seantw/DEBATE_LLM"
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+ ],
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+ "creator": {
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+ "@type": "Person",
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+ "name": "Sean Chuang",
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+ "url": "https://huggingface.co/seantw"
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+ },
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+ "keywords": [
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+ "100K - 1M",
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+ "csv",
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+ "Tabular",
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+ "Text",
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+ "Datasets",
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+ "Dask",
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+ "Polars",
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+ "Croissant",
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+ "arxiv:2510.25110",
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+ "🇺🇸 Region: US"
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+ ],
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+ "url": "https://huggingface.co/datasets/seantw/DEBATE_LLM",
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+ "conformsTo": "http://mlcommons.org/croissant/1.1",
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+ "rai:dataUseCases": [
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+ "Benchmarking opinion dynamics in multi-round debates and conversations.",
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+ "Evaluating role-playing LLM agents against human debate trajectories and interaction patterns.",
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+ "Research on argumentation, consensus building, and belief updating on controversial topics."
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+ ],
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+ "rai:dataLimitations": [
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+ "The dataset focuses on controversial opinion-based topics and should not be treated as representative of general everyday conversation.",
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+ "Some conversations are incomplete because of participant dropout.",
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+ "Some entries reflect automatic submissions caused by time limits.",
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+ "Empty rows can arise from concatenation of consecutive messages and should not be interpreted as literal empty participant utterances.",
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+ "The benchmark is appropriate for evaluating opinion-dynamics behavior in this task setting, but not for making direct claims about real-world persuasion effectiveness or population-level political behavior."
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+ ],
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+ "rai:dataBiases": [
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+ "Topic-selection bias: political, social, and belief-related controversies are overrepresented relative to neutral conversation.",
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+ "Human-sample bias: the participant pool may not be representative of the broader population in demographics, political orientation, topic familiarity, communication style, or willingness to revise opinions.",
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+ "Selection and self-selection bias: people who choose to participate in online debate studies may differ systematically from non-participants in engagement level, expressiveness, and tolerance for disagreement.",
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+ "Platform and task-design bias: online experimental settings, time limits, incentives, and interface constraints may shape how participants respond and may not reflect naturalistic offline or long-term discussions.",
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+ "Attrition bias: conversations with participant dropout or incomplete participation may systematically differ from fully completed conversations.",
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+ "The depth-versus-breadth design intentionally allocates uneven coverage across topics."
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+ ],
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+ "rai:personalSensitiveInformation": [
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+ "Free-text discussions and scalar opinion ratings on political, social, and belief-related topics may reveal sensitive beliefs or demographic proxies.",
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+ "The public release uses worker-style identifiers; authors should explicitly state whether any direct identifiers were removed or redacted before release."
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+ ],
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+ "rai:dataSocialImpact": "Potential positive impact: enabling reproducible evaluation of social reasoning and opinion-dynamics behavior in LLM agents. Potential negative impact: misuse for persuasion profiling, overclaiming real-world social competence, or benchmarking politically sensitive content without adequate safeguards.",
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+ "rai:hasSyntheticData": false,
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+ "prov:wasDerivedFrom": [],
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+ "prov:wasGeneratedBy": [
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+ "Data collection: original human multi-round conversation experiments on controversial opinion-based topics; see Appendix X for collection protocol, timeframe, and participant recruitment details.",
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+ "Preprocessing: raw experiment exports were cleaned and organized into raw and golden subsets; golden subsets require complete end-to-end participation, non-empty required fields, and validity-rate thresholds; see Appendix X.",
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+ "Annotation / derived fields: validity labels, agreement levels, slider values, and golden-data criteria are documented in Appendix X together with quality-control procedures."
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+ ]
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+ }