Precision Is Not Faithfulness: Coverage-Aware Evaluation of Grounded Generation with a Complete Oracle
arXiv:2606.09376v2 Announce Type: replace Abstract: Reference-free faithfulness metrics verify each atomic claim a model makes against ground truth, and are increasingly used to evaluate grounded generation. We show they share a blind spot: they measure only precision -- are the stated claims supported? -- and therefore reward abstention, since a model can score near-perfect faithfulness by saying almost nothing. We make this measurable using Formula 1 telemetry, a domain where strategic ground truth is derived deterministically and, crucially, completely: for each decision we know the full se
The proliferation of grounded generation models highlights a critical need for robust evaluation methodologies that accurately reflect model performance beyond superficial metrics.
This research reveals a fundamental flaw in current faithfulness metrics for AI, impacting how the reliability and completeness of grounded generation models are understood and developed.
The understanding of AI model evaluation shifts from solely rewarding precision to requiring a more comprehensive assessment that also considers coverage and completeness, penalizing abstention.
- · AI researchers focusing on robust evaluation
- · Developers of grounded generation models
- · Sectors requiring high-completeness AI outputs
- · AI models that prioritize brevity over comprehensiveness
- · Uncritical adopters of existing faithfulness metrics
AI models will be retrained or redesigned to optimize for completeness in addition to precision.
New standards for AI model evaluation will emerge, leading to more trustworthy generative AI applications.
Increased public and institutional confidence in AI-generated content, potentially accelerating AI adoption in critical domains.
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Read at arXiv cs.CL