# Human baselines and control words for tic detection > Source-anchored research for Human baselines and control words for tic detection. Run status: COMPLETED. Parent: [Measuring LLM lexical tics: excess vocabulary, baselines, drift](https://llms-explorer.com/tree/ai-isms-tic-measurement/) · 4 facets · 7 facts · page: https://llms-explorer.com/tree/human-baselines-and-control-words-for-tic-detection/ ## How it works - Kobak et al. build a counterfactual baseline by linear extrapolation from earlier years (q = p_{Y-2} + 2*max{p_{Y-2}-p_{Y-3},0}) and deliberately exclude 2023 because it may already be LLM-affected. (medium confidence) — [source](https://arxiv.org/html/2406.07016) ## Measurements and reference values - Control by word class: authors hand-annotated 900 excess words from 2013-2024 as content (about 51%) or style (about 45%); Covid-era excess was mostly content nouns, 2024 excess mostly style verbs and adjectives, which separates topical drift from LLM tics. (low confidence) — [source](https://arxiv.org/html/2406.07016) ## Problems, failure modes and limitations - Excess-word counts are only a lower bound on LLM use, and cannot distinguish LLM use from authors independently adopting LLM-preferred vocabulary. (low confidence) — [source](https://arxiv.org/html/2406.07016) ## Facts and statements - Excess words are flagged using two metrics, frequency gap and frequency ratio; thresholds were chosen so most pre-Covid years fall well below them, giving conservative estimates. (low confidence) — [source](https://arxiv.org/html/2406.07016) - Human baselines for model-output tic detection can be built from pre-2022 corpora (wordfreq for single words; Reddit creative writing and Project Gutenberg for n-grams in Antislop), with overuse expressed as a ratio of model frequency to baseline frequency. (medium confidence) — [source](https://arxiv.org/pdf/2510.15061) - Post-2021 web text is contaminated by generative-AI output, so frequency resources built from it no longer measure human language; wordfreq's maintainer stopped updating it for this reason, forcing baselines to pre-2022 archives. (medium confidence) (verified as of 2024-09-15) — [source](https://news.ycombinator.com/item?id=41578483) - Confound check: the hypothesis that 'delve' reflects Nigerian English annotators was not supported by International Corpus of English data (small corpus); RLHF-based Llama experiments fit better as the source of overrepresentation. (medium confidence) — [source](https://arxiv.org/html/2412.11385v1)