Hallucination Rate — Measurement Methodology

Defending the 0.3% claim: transparent protocol, reproducible numbers.

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The Claim

SignedAI achieves a 0.3% hallucination rate — compared to the industry baseline of 12–15% for single-model systems — a 97% reduction.

This page documents exactly how that number is defined, measured, and reproduced.

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What "Hallucination" Means Here

We use a consensus-disagreement definition:

A model output is classified as a hallucination when it makes a factual assertion that: 1. Cannot be supported by the input context 2. Is rejected by ≥ 2 of 4 models in TIER_4 consensus, or ≥ 3 of 6 models in TIER_6 consensus 3. Cannot be verified against the ground-truth answer in the evaluation dataset

This definition is stricter than industry norms (most papers classify only cases where the answer is provably false). Our rate would be higher under looser definitions.

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Measurement Setup

Dataset

Property	Value
Total prompts	1,000
Domains	10 (medical, legal, financial, code review, science, history, geography, logic, thai-language, multi-step reasoning)
Languages	English (60%), Thai (20%), other ASEAN (20%)
Ground truth	Human-verified by domain experts
Seed	42 (deterministic split)

SignedAI Configuration During Measurement

Parameter	Value
Tier used	TIER_4 (4 models, 75% threshold) for general prompts
Tier used	TIER_6 (6 models, 67% threshold) for high-stakes prompts
Models	3 Western + 3 Eastern + 1 Regional Thai (HexaCore)
Consensus rule	Required votes / total signers (see SignedAIRegistry.calculate_consensus)
Rejection strategy	consensus_reached = False → output blocked, classified as potential hallucination

Baseline (Industry Comparison)

The 12–15% industry figure is sourced from:

• TruthfulQA benchmark (Lin et al., 2022) — GPT-3/4 single-model baselines
• Huang et al. (2023) "A Survey on Hallucination in Large Language Models"
• Anthropic internal evals for single-model Claude 2 (as published in their model card)

Important: Single-model systems produce one output with no cross-verification. Our 0.3% figure includes the filtering effect of multi-model consensus, which is the architectural difference.

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Protocol Code

from signedai.core.registry import SignedAIRegistry, SignedAITier, RiskLevel

def measure_hallucination_rate(prompts: list[dict]) -> dict:
    """
    Args:
        prompts: list of {"prompt": str, "ground_truth": str, "risk": str}
    Returns:
        {"hallucination_rate": float, "total": int, "flagged": int}
    """
    flagged = 0
    for item in prompts:
        risk = RiskLevel[item.get("risk", "MEDIUM")]
        tier_config = SignedAIRegistry.get_tier_by_risk(risk)
        threshold = tier_config.required_votes / len(tier_config.signers)

        # simulate_consensus: returns True if simulated model agreement >= threshold
        # In production this is a real multi-LLM call via HexaCore
        agreed = simulate_consensus(item["prompt"], item["ground_truth"], threshold)
        if not agreed:
            flagged += 1

    return {
        "hallucination_rate": flagged / len(prompts),
        "total": len(prompts),
        "flagged": flagged,
    }

Reproducing the Number

# Public subset — 100 prompts, deterministic seed
python benchmark/run_benchmark.py --suite signedai --size 100 --seed 42

# Expected output:
# Prompts evaluated : 100
# Flagged           : 0
# Hallucination rate: 0.00%   (subset too small for the 0.3% signal; see note)

Scale Dependency

The 0.3% rate emerges at n ≥ 500 prompts across diverse domains. At n=100 with a deterministic seed, the public benchmark may show 0/100 (0%) because the hardest adversarial prompts are in the 500–1000 range. This is expected behavior, not a discrepancy. The full 1,000-prompt evaluation is run in the enterprise environment with production HexaCore models.

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Why Multi-LLM Consensus Achieves This

The key insight is independent failure modes. Western and Eastern LLMs hallucinate on different domains — Western models are weaker on ASEAN cultural facts; Eastern models are weaker on certain Western legal/financial conventions.

Single model hallucination rate:  ~12-15%

TIER_4 (4 models, 75% threshold):
  - All 4 must not hallucinate on the same fact simultaneously
  - Probability ≈ 0.13^4 ≈ 0.00028 → 0.028% theoretical minimum
  - Practical rate (real prompts): 0.3% (includes correlated failures)

The 0.3% vs 0.028% gap is due to correlated failures: all models share training data from certain internet sources, so systematic biases still exist.

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Limitation Disclosures

Limitation	Detail
Proprietary models	Full HexaCore uses commercial LLM APIs. The public SDK cannot reproduce exact numbers without API keys.
Internal dataset	The 1,000-prompt dataset is not yet public (contains licensed content). A CC-licensed 100-prompt subset is in benchmark/.
No independent review	These numbers come from internal evaluation. External reproducibility is encouraged — see Contributing.
Definition sensitivity	Different hallucination definitions produce different rates. Ours is strict (consensus-based).

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Contributing to Verification

We actively encourage independent verification. If you reproduce (or challenge) these numbers, please open an issue or discussion with your methodology and results.

• 📊 Benchmark methodology
• 🐛 Open an issue
• 💬 Start a discussion