New Toolkit Quantifies LLM Hallucination Risk Without Model Retraining

Large language models' troubling tendency to hallucinate facts just became measurable and controllable without costly retraining. The newly open-sourced Hallucination Risk Calculator introduces a mathematical framework that transforms raw prompts into quantified reliability scores using OpenAI's existing APIs.

The Information-Theoretic Backbone

At its core, the toolkit applies the Expectation-level Decompression Law (EDFL) - a novel approach that calculates hallucination risk through prompt degradation experiments. By generating "skeleton" versions of prompts through strategic redactions (called "rolling priors"), it measures the information lift between weakened and original prompts:

Δ̄ = (1/m) ∑ clip₊(log P(y) - log Sₖ(y), B)

This information budget (measured in nats) enables two critical outputs:
1. A provable hallucination risk bound (RoH ≤ 1 - p_max)
2. An ANSWER/REFUSE decision against target Service Level Agreements (SLAs)

The system's true innovation lies in its dual-prior approach: Using worst-case priors ($q_{lo}$) for strict SLA gating while leveraging average priors ($q̄$) for realistic risk bounds - ensuring both safety and practicality.

Two Operational Modes

1. Evidence-Based Workflow

For contexts with provided references, the toolkit erases evidence blocks while preserving structural cues. A sample implementation:

from scripts.hallucination_toolkit import OpenAIBackend, OpenAIItem, OpenAIPlanner

backend = OpenAIBackend(model="gpt-4o-mini")
item = OpenAIItem(
    prompt="""Task: Answer based strictly on evidence...
Evidence: [content]""", 
    fields_to_erase=["Evidence"]
)
planner = OpenAIPlanner(backend)
metrics = planner.run([item], h_star=0.05)  # 5% max hallucination target
print(f"Decision: {'ANSWER' if metrics[0].decision_answer else 'REFUSE'}")

2. Closed-Book Approach

For bare queries, it applies semantic masking to entities, numbers, and dates across progressive degradation levels:

item = OpenAIItem(
    prompt="Who won the 2019 Nobel Prize in Physics?",
    skeleton_policy="closed_book",
    n_samples=7  # Enhanced stability
)
metrics = planner.run([item], h_star=0.05)
print(f"Hallucination risk bound: {metrics[0].roh_bound:.3f}")

Why Developers Should Care

This approach solves critical real-world problems:
- No retraining needed: Works with existing OpenAI APIs
- Transparent math: Provides audit trails with SLA certificates
- Tunable conservatism: Adjust safety margins (default: 0.2 nats) and target SLAs
- Cost-effective: ~$0.03/query using gpt-4o-mini

Validation shows the framework behaves intentionally - refusing arithmetic queries (where pattern recognition survives masking) while answering entity-rich questions. As AI researcher Dr. Leo Chen noted: "This isn't a bug but a feature. The system prioritizes safety through worst-case guarantees while providing realistic average-case bounds."

Implementation Reality Check

The toolkit offers multiple deployment pathways:

Critical tuning considerations include:
- Sampling ≥5 times per variant
- Temperature between 0.2–0.5
- Masking strength adjustments for problematic domains

The mathematical rigor behind this approach represents a paradigm shift - moving from heuristic hallucination mitigation to information-theoretic guarantees. As LLMs penetrate sensitive domains like healthcare and finance, such auditable safety frameworks may become as essential as testing suites are for traditional software.

Developed by Hassana Labs and available under MIT License at GitHub. Methodology based on Compression Failure in LLMs: Bayesian in Expectation, Not in Realization (NeurIPS 2024 preprint).