language:
- en
license: apache-2.0
size_categories:
- 100K<n<1M
task_categories:
- text-generation
pretty_name: Debate & Argumentation SFT (100K)
tags:
- argumentation
- debate
- persuasive-writing
- critical-thinking
- steel-manning
- rhetoric
- sft
- supervised-fine-tuning
- synthetic
configs:
- config_name: default
data_files:
- split: train
path: debate-argumentation-sft-100k.jsonl
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, and logical structure. Models trained primarily on factual Q&A tend to:
- Present both sides of an issue without committing to a position when asked for a persuasive argument
- Fail to steelman opposing views (describe them in their weakest form)
- Produce rebuttals that miss the actual claim being made
- Write policy arguments that lack specificity about mechanisms and tradeoffs
This dataset trains models to engage with controversial topics at a higher intellectual level: arguing a position persuasively when asked, charitably representing opposing views, and providing substantive policy analysis.
Dataset Description
100,000 conversations across 7 task types and 17 topics:
Task Type Distribution
| Task Type | Count | % | Description |
|---|---|---|---|
persuasive_essay |
~31,250 | 31% | Argue a position with premises, evidence, and conclusion |
steel_man |
~18,750 | 19% | Present the strongest version of an opposing view |
rebuttal |
~18,750 | 19% | Directly counter a specific argument |
counter_argument |
~12,500 | 13% | Argue against a commonly assumed position |
policy_memo |
~6,250 | 6% | Professional policy analysis with recommendations |
socratic |
~6,250 | 6% | Socratic dialogue that uncovers tensions and distinctions |
debate_opener |
~6,250 | 6% | Structured opening statement for a formal debate |
Topics Covered
Workplace & Economics:
- Permanent remote work options
- Four-day workweek policy
- Corporate sabbatical programs
- Meeting length defaults (25-minute default)
- Universal Basic Income
Technology & Policy:
- AI regulation (pharmaceutical-style approval)
- Open source AI development
- Germline gene editing
- Electric vehicle mandate pace
- Data privacy legislation
Society & Values:
- Social media bans for under-16
- Meritocracy critique
- Surveillance and privacy (nothing-to-hide argument)
- Justice (equal outcomes vs. equal opportunity)
Science & Energy:
- Nuclear power for decarbonization
- AI creativity and authorship
Format
{
"conversations": [
{
"from": "human",
"value": "Write a persuasive essay arguing that companies should offer permanent remote work options for all knowledge workers. Make a strong case — I want to be convinced."
},
{
"from": "gpt",
"value": "The case for permanent remote work rests on three pillars that, taken together, are difficult to dismiss: demonstrated productivity, talent market realities, and equity...
[full persuasive argument with premises, evidence, and conclusion]"
}
],
"metadata": {
"task_type": "persuasive_essay",
"topic": "remote_work"
},
"id": "abc123"
}
Key Properties of Responses
1. Position commitment: When asked to argue a position, responses argue it — they don't hedge with "on the other hand" or present a false balance. A persuasive essay is persuasive.
2. Argument structure: Responses identify premises, provide evidence or reasoning for each, and build to a conclusion. Not just assertions.
3. Charitable representation (steel-manning): Steel-man responses present the opposing view in its strongest form — the version that proponents would recognize and endorse, not a strawman.
4. Targeted rebuttals: Rebuttal responses identify the specific claim being rebutted and address the actual argument, not a weaker version of it.
5. Intellectual rigor in dialogue: Socratic dialogues surface genuine tensions, make distinctions, and avoid strawmanning either position in the dialogue.
6. Policy specificity: Policy memos include mechanisms, tradeoffs, and implementation considerations — not just values statements.
Use Cases
- SFT fine-tuning for improved argumentation and persuasive writing
- Training AI debate assistants and writing aids
- Improving model performance on argument quality benchmarks
- Building AI tutors for rhetoric and critical thinking education
- Complementary to alignment datasets: models should argue positions when asked, not just describe them neutrally
- Paired with
sycophancy-reduction-dpo-100kfor models that argue well but don't cave to pushback
License
Apache 2.0