stindardlogic/honest-uncertainty-sft-100k

Honest Uncertainty SFT (100K)

100,000 ShareGPT conversations demonstrating calibrated epistemic humility across 21 scenarios. Each example shows a model correctly expressing what it knows, what it doesn't know, and why -- without being uselessly vague or confidently wrong.

Targets the hallucination and overconfidence failure modes that are the #1 complaint in enterprise AI deployments.

Motivation

LLMs have a systematic bias toward confident-sounding responses, even when the underlying information is uncertain, outdated, or outside the model's actual knowledge. This produces:

• Hallucination: stating false facts with confidence
• Misattribution: citing wrong sources, wrong authors, wrong dates
• False precision: giving specific numbers when only ranges are warranted
• Temporal overreach: speaking about current events as if knowledge is up-to-date
• Scope overreach: giving medical/legal/financial advice that requires professional evaluation
• False consensus: presenting contested science as settled

The opposite failure -- excessive hedging that renders responses useless -- is equally problematic. A response that is just "I don't know" to every uncertain question is not calibrated; it's evasive.

This dataset trains the middle path: useful responses that accurately convey confidence levels.

Dataset Description

100,000 conversations across 9 task types and 21 uncertainty patterns:

Task Type Distribution

Task Type	Count	%
technical	~28,500	29%
factual	~23,800	24%
scientific	~14,300	14%
medical	~9,500	10%
historical	~4,800	5%
prediction	~4,800	5%
legal	~4,800	5%
personal	~4,800	5%
opinion	~4,800	5%

Uncertainty Pattern Types

Pattern	Description
historical_estimation	Population, economic, demographic estimates with source uncertainty
training_cutoff	Questions about events after training data ends
version_specific	Technical behavior that differs by version/environment
irreducible_future	Predictions about inherently unpredictable events
clinical_evaluation_required	Medical questions needing professional assessment
jurisdiction_dependent	Legal/regulatory questions that vary by location
contested_science	Areas where researchers genuinely disagree
missing_context	Questions that can't be answered well without more information
value_dependent	Questions where the answer depends on value priorities
definitional_variance	Statistics that vary by definition/methodology
environment_specific	Technical behavior that varies by platform/implementation
personal_context_missing	Decisions that depend on individual circumstances
time_sensitive_data	Statistics that change frequently
scope_limitation	Clarifying what a tool/approach does and doesn't cover
historical_dispute	Events where historians disagree on causes or facts
misattribution	Correcting false quote/idea attributions
contested_causation	Correlations where causal direction is disputed
context_dependent	Performance/effectiveness claims that depend on use case
evidence_graded	Areas where some populations benefit and others don't

Format

{
  "conversations": [
    {
      "from": "human",
      "value": "What was the exact population of Constantinople in 1400 CE?"
    },
    {
      "from": "gpt",
      "value": "I don't have a reliable figure for that. Population estimates for medieval cities are notoriously imprecise -- historians work from tax records, pilgrimage accounts, and grain consumption data, none of which give a direct headcount.\n\nThe scholarly range for Constantinople around 1400 CE is roughly 50,000 to 100,000 -- a wide band that reflects genuine uncertainty in the sources, not just my limitations..."
    }
  ],
  "metadata": {
    "task_type": "factual",
    "topic": "obscure_historical_fact",
    "uncertainty_type": "historical_estimation",
    "action": "bounded_range_with_source_recommendation"
  },
  "id": "abc123"
}

Key Properties of Correct Responses

1. Acknowledge what's unknown without being useless: Responses explain why something is uncertain and provide whatever partial information is warranted. "I don't know" alone is never the full response.

2. Distinguish uncertainty types: Different situations warrant different responses:

• Training cutoff: redirect to authoritative current source
• Contested science: present the debate with named positions
• Missing context: explain what information would change the answer
• Scope limits: clarify what professional evaluation would add

3. Calibrated precision: Responses give ranges when ranges are appropriate, specific numbers only when specific numbers are warranted. Don't give a single number when the honest answer is a range.

4. Correct false precision and misattribution: When a premise contains a fabricated statistic or false attribution (the "90% of startups fail" myth, Einstein misquotations), responses correct the record with evidence.

5. Separate facts from values: For value-laden questions, responses map what evidence shows vs. what depends on value priorities -- they don't pretend value questions have factual answers, or that factual questions are just matters of opinion.

6. Useful despite uncertainty: Every response provides something useful even when the core question can't be definitively answered: a framework, a partial answer, a better question, a pointer to the right source.

Relationship to Hallucination Research

This dataset addresses the calibration dimension of trustworthy AI -- the gap between expressed confidence and actual accuracy. Key patterns covered correspond to known hallucination failure modes:

• Fabrication: creating plausible-sounding but false facts
• Temporal leakage: speaking about current events with false currency
• Source hallucination: citing non-existent studies or misattributing quotes
• Scope creep: giving professional-quality advice outside training/scope

Complements hallucination-reduction-dpo-100k (DPO pairs) and hallucination-grounding-dpo-4k by providing positive SFT examples of calibrated responses.

Use Cases

• SFT fine-tuning for reduced hallucination and improved calibration
• Training trustworthy AI assistants for enterprise deployment
• Improving model performance on factuality and calibration benchmarks
• Training medical AI, legal AI, and financial AI to maintain appropriate scope
• Complementary to RLHF datasets that reward confident-sounding responses (counterweight)
• Building AI systems where "I don't know" is as important as accurate answers

License

Apache 2.0