# Peer-review praise register Part of [The AI Tells Index](https://feedsquad.com/ai-tells). A tell signals low effort. It does not identify an author. Skilled writers produce every shape listed here, some of them daily, and automated detectors misread those writers for it at rates measured above 60 percent on non-native English prose. Nothing in this index proves that a machine wrote anything. Read an entry as one piece of evidence to weigh against the false-positive notes printed beside it. ## Facts - Id: review-praise-register - Category: Lexical (lexical) - Subcategory: evaluative-praise - Also known as: commendable, meticulous, meticulously, compelling, noteworthy, invaluable - Status: Fading. Signal is weakening, usually because model vendors trained the habit out. - Severity: medium - Evidence grade: peer-reviewed (a published study measures the pattern) - Languages: en - Added: 2026-08-15 - Updated: 2026-08-17 - Page: https://feedsquad.com/ai-tells/review-praise-register ## Description Evaluative praise vocabulary that rose sharply in conference peer review after 2022. Liang and colleagues measured review text at four AI conferences and estimated that between 6.5 and 16.9 percent of it was substantially modified by a language model after ChatGPT, against a pre-ChatGPT control of 1.6 to 2.4 percent. The control is what makes that number citable, and it is the number this entry rests on. Per-word fold changes appear in the paper body and we did not confirm them at page level, so they are not printed here. ## Why it reads as machine-written Praise vocabulary is what a text produces when it has read something and has nothing to say about it. A commendable effort and a compelling narrative are verdicts with no reasons attached, and a reviewer with a reason usually leads with the reason. ## Detection Type: deterministic (a regular expression, run as written) Scope: sentence ECMAScript regular expression. The build validator compiles it and tests it against this entry's own specimens. ```regex \bcommendable\s+(?:effort|work|job|attempt|contribution)\b|\bmeticulous(?:ly)?\s+(?:crafted|curated|researched|documented|designed)\b|\ba\s+(?:truly\s+)?compelling\s+(?:case|narrative|argument|read)\b|\b(?:is|are|was|were)\s+(?:a\s+)?(?:noteworthy|invaluable)\s+(?:contribution|addition|resource)\b ``` Flags: gi ## Examples Constructed specimens. Written for this index. Never quoted from anyone's posts. ### Specimen 1 Before, exhibiting the tell: > The authors have done a commendable job, and the meticulously crafted methodology makes for a compelling read. The framing is timely and the argument is organised well throughout. The literature review is thorough and situates the contribution appropriately within the wider body of work. A few small clarifications would strengthen the presentation, though none of them touch the substance of what is claimed, and the reviewer has no material concerns to raise on that front. The manuscript would make a welcome addition to the issue. After, repaired: > The sampling frame excludes anyone who left the platform before 2024, which is the population the paper is about. Table 3 would settle it if it reported the attrition count. Note: The repair is the objection. It is also shorter, which is usual. ## False positives Who legitimately writes this way. Reviewers, referees, examiners and anyone writing a school report are required to evaluate, and evaluation vocabulary is the tool for it. Recommendation letters are built from this register, and a letter writer who avoids it damages the candidate. Meticulous is literal in conservation, watchmaking, archaeology and laboratory technique, where it describes an observable working method rather than a compliment. Yakura and colleagues measured these words rising in spontaneous podcast speech after 2022, which means a human writing commendable in 2026 may simply have heard it. The discriminator is whether a reason follows the verdict within a sentence or two. A hit on a peer review is a prompt to ask what the reviewer noticed, and never a finding about who wrote it. ## Model attribution Measured across review text at four machine learning conferences without attribution to a vendor. Liang estimates the share of substantially modified text in a corpus and makes no claim about any single review. ## Sources 1. Liang et al., Monitoring AI-Modified Content at Scale, ICML 2024 (arXiv:2403.07183) https://arxiv.org/abs/2403.07183 (tier: peer-reviewed; accessed 2026-08-14) 2. Kobak et al., Delving into LLM-assisted writing in biomedical publications, Science Advances 11(27) https://www.science.org/doi/10.1126/sciadv.adt3813 (tier: peer-reviewed; accessed 2026-08-14) 3. Yakura et al., Empirical evidence of LLM influence on human spoken communication (arXiv:2409.01754) https://arxiv.org/abs/2409.01754 (tier: primary-doc; accessed 2026-08-14) ## Status history Ids are permanent. A retired tell keeps its id and its page. - 2026-08-15, Fading: Fading. Liang measured the rise in reviews written between 2023 and 2024, and the vocabulary has since been named in every venue that discusses this, including conference policies. Yakura documents the same words entering unscripted human speech, which drains a word-level marker of information regardless of what any model does next. ## License CC BY 4.0. https://creativecommons.org/licenses/by/4.0/ Attribution: The AI Tells Index, feedsquad.com/ai-tells Reuse the data, including commercially. Keep the attribution line. --- Dataset version 1.0.0. Schema version 1. Part of [FeedSquad](https://feedsquad.com). Built by [Herman Foundry](https://hermanfoundry.com) from Levi, Finnish Lapland.