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Factuality Evaluation and Citation Quality: Proving Grounded Answers
How to evaluate factuality and citation quality for LLM answers in high-stakes environments.
Why Factuality Is a Separate Metric
Correct-sounding answers are not enough when users need evidence.
Factuality evaluation ensures answers are grounded and verifiable.
Citation Accuracy and Coverage
Measure whether citations actually support the claim.
Track coverage: are all critical facts supported by sources?
Human Review Loops
Human reviewers remain the gold standard for factuality checks.
Use consistent rubrics to reduce variance across reviewers.
Automated Verification
Use claim verification models or rule-based checks for scalability.
Automated checks should prioritize precision to avoid false assurance.
Operational Monitoring
Track citation errors and user reports in production.
Use those signals to refine retrieval and answer generation.
Claim Decomposition
Split answers into atomic claims before checking sources.
Map each claim to a citation so coverage gaps are visible.
Flag unsupported claims automatically for reviewer follow-up.
Use templated checklists to keep human review consistent.
Group claims by risk level to prioritize the highest impact checks.
Track partial support when sources only cover part of a claim.
Record ambiguous claims separately to improve prompt guidance.
Log missing evidence to inform retrieval improvements.
Scoring and Benchmarks
Define precision, recall, and coverage targets for citations.
Use a held-out factuality set to avoid overfitting to review data.
Score at both claim level and answer level for clarity.
Measure reviewer agreement to validate rubric quality.
Include latency impact so factuality checks stay practical.
Track regressions when retrieval or prompt templates change.
Publish scorecards alongside model releases for transparency.
Set release gates for critical workflows based on factuality scores.
Compare factuality scores across domains to surface weak areas.
Add confidence thresholds to flag low-evidence answers automatically.
Review false positives to keep verification rules from overblocking.
Keep a small gold set to validate evaluator consistency over time.
Calibrate scoring with domain experts for regulated environments.
FAQ: Factuality
Do citations guarantee truth? No, but they enable verification.
Can I automate factuality? Partially; human review is still needed for critical tasks.
What is the fastest win? Require citations for all factual claims.
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