De-slopping LLM text

slop-fuck.txt

ANGLES: 4   SOURCE CHECKS: 13
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SUMMARY: De-slopping LLM text is mostly a structural and thinking problem, not a punctuation problem. Naive negative prompts ("don't use em dashes," "sound less like AI," blanket word blacklists) fail for two reasons: the model has no replacement target so it substitutes another tell, and the deeper issue is generic, thinking-free scaffolding that survives any word swap. What works is a staged process: fix structure first (kill default intros/recaps, symmetric subheads, list-dumps, signposting), then apply positive language rules that say what to write instead, then a separate judge-model pass that only diagnoses, then a human pass for argument and voice. The most reliable single heuristic is the "one-line test": compress each paragraph to one sentence and read the result as an outline; if it reads like a generic template, no word-level edit will save it. Editors uniformly recommend editing in passes (one tell per pass, most-visible first) and giving the model constraints and voice samples up front so you prevent slop rather than polish it.

* Naive em-dash ban (what does NOT work)
  WHAT: Telling the model 'don't use em dashes' or blacklisting words with no replacement. The model needs to join two clauses somewhere, so it swaps the em dash for another tic (colon reveal, semicolon aphorism) and the text still reads as AI. Blanket 'sound less like AI' fails the same way. Example failure: banning em dashes yields 'It learns as it goes: that's the point.'
  FIX:  Give a positive replacement target: 'If an em dash would appear, use a period for a separate sentence or a comma for a continuing thought.' Better, fix the sentence's job rather than its punctuation. Root cause is generic thinking, so specify voice samples and structure up front, not a forbidden-character list.
* Binary contrast / manufactured antithesis
  WHAT: 'It's not X, it's Y' and its variants ('not just A, but B', 'A is useful, but B is what matters'). Grep for 'not just', 'isn't about', "it's not". Example: 'This isn't a tool. It's a teammate.'
  FIX:  State the claim directly. 'The agent handles the whole review loop, not only the diff.' If the point is Y, write Y plainly and then give the mechanism; drop the discarded X entirely.
* Throat-clearing openers and filler transitions
  WHAT: Sentences that start with 'Here's the thing', 'Let me be clear', 'It's worth noting that', 'In today's fast-paced world', 'Let's dive in'. Also mid-body signposting: 'Now that we've explored...', 'As mentioned earlier...'. Example: 'It's worth noting that latency dropped.'
  FIX:  Delete the opener and lead with the concrete fact: 'Latency dropped 40ms.' Cut every 'as mentioned' recap; if the reader already read it, don't restate it.
* Self-answered rhetorical question / colon reveal
  WHAT: 'The result? A faster loop.' or 'The best part: it learns.' A question or setup immediately answered by the writer, or a colon used as a drumroll. Grep for '? ' mid-paragraph and 'part:' / 'kicker:' / 'result:'.
  FIX:  Convert to a plain declarative: 'The loop got faster.' Reserve colons for lists and real definitions, not suspense.
* Triadic rhythm and dramatic fragments
  WHAT: Patterns of three ('faster, cheaper, simpler') used as marketing cadence, plus punchy sentence fragments as emphasis: 'That's it. That's the whole thing.' Also fake-profound closers: 'The future isn't coming. It's already here.'
  FIX:  Break the triad or make each item earn its place with specifics. Replace the fragment-drama with one ordinary sentence. Cut the grand closer; end on the last concrete point.
* Marketing vocabulary and empty adjectives
  WHAT: delve, realm, tapestry, seamless, robust, comprehensive, powerful, leverage, harness, navigate, embark, pivotal, groundbreaking, transformative, testament to, ever-evolving landscape. Adjectives carrying no data: 'remarkably fast', 'critical improvement'.
  FIX:  Keep an adjective only if it adds scale or measurable information, else delete. Replace the vague claim with a number: not 'remarkably improved correlation' but 'r = 0.267, p < 0.0001'. Prefer plain verbs (use, build, cut) over leverage/harness.
* Importance puffery and weasel attribution
  WHAT: 'marks a pivotal moment', 'a testament to the team's commitment to innovation', 'highlighting a dedication to quality'; plus sourceless authority: 'experts agree', 'studies show', 'a growing body of literature'. Example: 'This release is a testament to our focus on users.'
  FIX:  Cut the puffery sentence entirely; show the thing instead of asserting its significance. Replace 'studies show' with the specific study and finding, or drop the claim.
* Structural symmetry: templated subheads and list-dumps
  WHAT: Sections named 'Understanding X', 'The Importance of Y', 'The Future of Z'; every section the same length ending in a mini-summary; developed argument replaced by bullet lists; a conclusion that recaps each section. This survives all word-level edits, so it's the highest-priority tell.
  FIX:  Fix structure before wording. Write your own outline first and tell the model to fill it without adding intro/conclusion. Convert bullet dumps back to paragraphs, vary paragraph length, and delete the section-by-section recap. Run the one-line test: compress each paragraph to a sentence; if the outline reads like a generic template, rebuild it.
* Synonym cycling and same-idea restatement
  WHAT: Saying one thing three ways with rotating synonyms to fill space: 'It's efficient. It streamlines the work. It makes things faster.' Also generic examples that would fit any topic.
  FIX:  Keep the single strongest statement and delete the restatements. Replace generic examples with one specific, checkable instance from the actual domain.

  SRC: De-slop the text you shouldn't be writing anyway | https://blog.stephenturner.us/p/deslop | 2025 | blog
       Scientist-editor's tell list (filler transitions, 'the ever-evolving landscape', em dashes every line, self-answered questions, 'not X, it's Y') plus a fix pattern: replace vague intensifiers with act
  SRC: How to Clean Up AI-Generated Drafts Without Sounding Like ChatGPT | https://www.louisbouchard.ai/ai-editing/ | 2025 | blog
       Concrete three-phase system: structure first (own outline, no added intro/recap, kill signposting and templated subheads), then positive language rules as explicit prompt constraints, then a two-model
  SRC: Use My /No-AI-Slop Skill to Remove 20+ Patterns of AI Slop From Your Writing (Peter Yang) | https://creatoreconomy.so/p/use-my-no-ai-slop-skill-to-remove-20-ai-slop-patterns | 2025 | blog
       Named catalog of discrete tells to grep for: binary contrasts, throat-clearing openers, faux-insight setups, colon reveals, dramatic fragments, importance puffery, weasel attribution, synonym cycling,
  SRC: How to Write with AI, a Guide to Fewer Em-Dashes | https://elliestoolbox.substack.com/p/how-to-write-with-aia-guide-to-fewer | 2025 | blog
       Argues the em-dash ban fails because the real problem is outsourced thinking, not punctuation; AI meets the sophistication you bring it. Fix is a positive process (you structure and draft, AI polishes
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SUMMARY: Across 2023-2026 stylometric and statistical detection research, the strongest machine-vs-human signal is not any single word but variance: LLM prose is over-even. Sentence lengths cluster tightly (low burstiness), token-level perplexity stays low and flat, lexical choices stay in a "safe" high-probability zone, and stylistic features show reduced intra-document and intra-domain variance. Studied directional findings: AI text uses more pronouns, more numbers/symbols/auxiliaries (a false "objectivity"), more mechanical discourse connectors, and skews toward joy while under-producing negative emotion (fear, disgust); humans show scattered sentence-length distributions, richer vocabulary, shorter constituents, and more optimized dependency distances. The recurring caveat, marked below, is that these markers drift as models and paraphrasers evolve, and lexical-diversity direction has flipped in some recent studies, so treat any single marker as a hint, not proof.

* Low burstiness (uniform sentence length)
  WHAT: AI keeps sentence lengths in a narrow band; human writing swings between very short and long sentences. Measured as low standard deviation of sentence length / low burstiness. Studied. Example AI: 'The system processes requests. It validates each input. It returns a response. The design is efficient.' (four sentences, ~5-7 words each).
  FIX:  Vary length hard. Follow a 3-word sentence with a 40-word one. Example: 'It validates every input, checks it against the schema, normalizes the casing, and only then hands it downstream. Then it responds. Fast.'
* Flat low perplexity / high-probability word choice
  WHAT: AI picks the statistically expected next word, so token perplexity is low and even; detectors flag this directly. Produces 'lexical safety': technically correct, emotionally flat, no risky or surprising word. Studied. Example: 'a wide range of powerful features that deliver significant value.'
  FIX:  Choose the less obvious word where it's more precise. Cut the safe abstraction for a concrete noun. Example: 'three features people actually use: undo, offline mode, and a fast search box.'
* Mechanical discourse connectors
  WHAT: Elevated rate of 'Additionally,' 'Furthermore,' 'Moreover,' 'In conclusion,' 'It is important to note that' at sentence heads. Studied (named as overused transitions). Example: 'Furthermore, this approach is scalable. Moreover, it is cost-effective. In conclusion, it is a strong choice.'
  FIX:  Delete most sentence-initial connectors; let ordering carry logic. Example: 'This approach scales, and it's cheap to run. That's why we picked it.'
* Over-even idea density
  WHAT: AI packs roughly constant information per paragraph (statistical averaging); humans alternate dense passages with light, digressive ones. Studied ('reduced idea density variation'). Every paragraph reads the same weight and rhythm.
  FIX:  Let some paragraphs be a throwaway aside or a single blunt line; make others carry the load. Deliberately break the even cadence.
* Pronoun / number / auxiliary inflation
  WHAT: In studied news comparisons LLMs used MORE pronouns, and more numbers, symbols, and auxiliary verbs than humans, projecting a false objectivity. Studied (Munoz-Ortiz et al.). Example AI: 'It is estimated that they will process approximately 40% more, and it should be noted that this is significant.'
  FIX:  Drop hedging auxiliaries and filler stats. Example: 'They'll process about 40% more.'
* Emotion skew toward joy, away from fear/disgust
  WHAT: LLM text carries more joy/positive affect and weaker negative emotions (fear, disgust) than human writing on the same topic. Studied. Reads relentlessly upbeat or neutral even about grim subjects.
  FIX:  Allow genuine negative register where the subject warrants it: irritation, worry, bluntness. Don't resolve every point on a positive note.
* Reduced stylistic variance across the document
  WHAT: Human writers vary style across sections (intro vs body vs conclusion); AI stays uniform, and 250+-feature profiling shows lower intra-document and intra-domain variance. Studied. The opening, middle, and closing all sound identical in rhythm and vocabulary.
  FIX:  Shift register intentionally between sections. A punchy opener, a dense technical middle, a plain close. Reuse fewer stock phrases across sections.
* Shorter constituents / over-tidy syntax
  WHAT: Humans use shorter constituents and more optimized dependency distances plus more varied dependency/constituent types; AI defaults to smooth, uniform, grammatically standardized clause structure. Studied. AI rarely produces fragments, false starts, or unusual word order.
  FIX:  Use a sentence fragment. Front a subordinate clause. Break a parallel triad. Let one sentence be slightly awkward the way real writing is.
* Formulaic phrase recycling
  WHAT: AI reuses similar phrase templates and structural scaffolds within a piece at higher rates than humans (higher lexical/structural repetition). Studied. Example: repeated 'When it comes to X...', 'plays a crucial role in', 'a testament to'.
  FIX:  Grep your own draft for any phrase used twice; rewrite one. Kill stock scaffolds like 'plays a crucial role in' and 'a testament to' entirely.

  SRC: Contrasting Linguistic Patterns in Human and LLM-Generated News Text | https://arxiv.org/abs/2308.09067 | 2024 | peer-reviewed paper (Artificial Intelligence Review, 2024)
       Studied, six LLMs across three families/four sizes vs human news: humans show more scattered sentence-length distributions, more vocabulary variety, shorter constituents, more optimized dependency dis
  SRC: Beyond Checkmate: Exploring the Creative Choke Points for AI Generated Texts | https://arxiv.org/pdf/2501.19301 | 2025 | peer-reviewed paper (EMNLP 2025 Main)
       Studied, segment-level analysis (intro/body/conclusion): AI most detectable where continuous flow is required; shows sentence-structure uniformity, reduced idea-density variation, mechanical discourse
  SRC: Explaining Generalization of AI-Generated Text Detectors Through Linguistic Analysis | https://arxiv.org/pdf/2601.07974 | 2026 | preprint (arXiv, 2026)
       Studied: AI shows lower sentence-length variance, reduced punctuation diversity, lower lexical diversity, and distinct function-word and POS patterns; explicitly warns these markers drift across model
  SRC: Feature-Based Detection of AI-Generated Text (search-surfaced summary) and A Survey on LLM-Generated Text Detection | https://arxiv.org/pdf/2310.14724 | 2023-2025 | survey / feature-analysis (arXiv)
       Studied and folklore mix: names the standard statistical markers (perplexity, burstiness, log-likelihood curvature, stylometric variance) and confirms low/flat perplexity and low burstiness as core AI
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SUMMARY: Empirical work has moved the "delve" phenomenon from folklore to measurement. The strongest study (Kobak et al., Science Advances 2025, preprint arXiv:2406.07016) analyzed 14.2M PubMed abstracts (2010-2024) and, borrowing the "excess mortality" method, found ~280 excess words spiking abruptly in 2024, overwhelmingly verbs (66%) and adjectives (18%) rather than the content nouns that spiked during COVID. The biggest jumps are rare style words: "delves" ran ~25x its pre-LLM baseline, "showcasing" ~9x, "underscores" ~9x. A follow-up (Juzek & Ward, arXiv:2412.11385) names a canonical set of 21 focal words (delve, intricate, meticulous, tapestry, realm, pivotal, testament, underscore, etc.) and tests, inconclusively, whether RLHF explains the overuse. A PubMed co-occurrence analysis found "delve" and "underscore" together in 98.8% of a co-occurring set only in 2023-2024, never across the prior 174 years. Single-word lists are well measured; multi-word phrase tells (it's worth noting, in conclusion) are community-observed and only partly backed by the frequency data, marked as such below.

* The 21 focal words (measured, high-confidence)
  WHAT: These content words spike as a cluster in post-ChatGPT text and are the most grep-able single-word tells. Juzek & Ward's canonical set: delve, intricate, commendable, meticulous, surpass, elevate, foster, tapestry, realm, navigate, landscape, pivotal, resonate, testament, underscore, showcasing, compelling, paramount, crucial, unwavering, alignment. Example: 'This study delves into the intricate tapestry of the immune landscape, underscoring the pivotal role of...' A human specialist rarely stacks three or four of these in one sentence.
  FIX:  Swap for the plain verb/noun: 'delve into' -> 'look at' / 'study'; 'underscore' -> 'show'; 'pivotal' -> 'key' or cut it; 'tapestry/landscape/realm' -> name the actual thing ('the set of immune cells', not 'the immune landscape'). Delete 'meticulous', 'commendable', 'compelling' outright.
* Rare style verbs run 9-28x baseline
  WHAT: The clearest statistical fingerprints are low-frequency verbs whose usage exploded. Measured frequency ratios vs pre-LLM baseline: delves r=25-28, underscores r=9-14, showcasing r=9-11. Because these were rare before 2023, even a few occurrences flag a text. Example: 'Our results showcase a novel mechanism and underscore its relevance.'
  FIX:  Use ordinary verbs: showcase -> show/present; underscore -> highlight or just state the point directly. 'Our results show a new mechanism and explain why it matters.'
* Verb/adjective inflation, not noun content
  WHAT: Kobak et al. found the 2024 excess vocabulary was 66% verbs and 18% adjectives, unlike genuine topic-driven shifts (e.g. COVID) which are dominated by content nouns. The tell is stylistic ornament words, not new subject-matter words. Example: an abstract heavy on 'highlighting', 'emphasizing', 'enhancing', 'exhibiting', 'boasting' but light on concrete nouns.
  FIX:  Cut adverbial/verb padding and add concrete nouns and numbers. Replace 'boasts a high accuracy' with 'reaches 94% accuracy'. If a sentence's only new words are style verbs, it is probably empty.
* Common-word upticks: potential, crucial, findings, notably, comprehensive
  WHAT: Beyond rare words, high-frequency words show measurable 'gap' increases (delta): potential d=0.041, findings d=0.027, crucial d=0.026. Kobak's high-impact set also includes across, additionally, comprehensive, enhancing, exhibited, insights, notably, particularly, within. Individually innocuous but they pile up. Example: 'Notably, these findings offer comprehensive insights into the potential mechanisms, particularly within...'
  FIX:  Prune the connective/booster words: 'notably', 'additionally', 'particularly', 'comprehensive' can almost always be deleted. 'offer insights into' -> 'show'; 'potential mechanisms' -> name the mechanism or say you don't know it yet.
* Modified-Z-score movers: boast, primarily, surpass, commendable
  WHAT: Matsui (Perspectives on Medical Education 2025) ranked terms by modified Z-score in 2024 PubMed; top movers were delve, underscore, primarily, meticulous, boast, commendable, showcase, surpass (103 terms crossed Z >= 3.5). 'boast(s)' is also a strong tell for AI product/marketing register. Example: 'The platform boasts a commendable throughput and primarily surpasses prior methods.'
  FIX:  'boasts' -> 'has'; 'commendable' -> give the number; 'surpasses' -> 'beats' or 'is faster than'; 'primarily' -> 'mostly' or cut.
* The delve+underscore co-occurrence signature
  WHAT: A PubMed study found 'delve' and 'underscore' appeared together in ~98.8% of a co-occurring set (1,284 of 1,299 articles), and the triple delve+realm+underscore co-occurred only in 2023-2024 across records back to 1849. Two or more focal words in the same short passage is a far stronger signal than any one alone. Example: 'We delve into the realm of X, underscoring its importance.'
  FIX:  When editing suspected AI text, treat clusters, not lone words, as the flag. In your own writing, if you catch two focal words in a paragraph, rewrite the paragraph in plain terms rather than swapping one word.
* Multi-word phrase tells (community-observed, weakly measured)
  WHAT: Phrases widely reported as AI tells: 'it's worth noting', 'in conclusion', 'it is important to note', 'navigating the landscape/complexities', 'a testament to', 'plays a pivotal/crucial role', 'rich tapestry', 'in the realm of'. Honest caveat: single-word components (navigate, landscape, testament, pivotal, realm, tapestry) ARE in the measured focal-word lists, but the exact multi-word phrasings come mostly from practitioner observation, not the frequency papers. Example: 'It's worth noting that this plays a pivotal role in navigating the complex landscape of...'
  FIX:  Delete meta-hedges ('it's worth noting', 'it is important to note') and formulaic closers ('in conclusion') entirely; state the point. 'plays a pivotal role in' -> 'matters for' or 'drives'. 'a testament to X' -> 'shows X'.
* Why it happens: not architecture, likely RLHF
  WHAT: Juzek & Ward found no evidence that model architecture, algorithm, or raw training-data frequency causes the overuse; their inconclusive but leading hypothesis is RLHF (human preference tuning) nudging outputs toward these words. Practical upshot: these are fashion words baked in by preference tuning, not by the topic, which is why they read as generic across every subject.
  FIX:  Because the cause is register/tuning, the fix is register: write the way a busy domain expert types (concrete, terse, number-first), the opposite of the polished-essay register RLHF rewards.

  SRC: Delving into LLM-assisted writing in biomedical publications through excess vocabulary (preprint title: Delving into ChatGPT usage in academic writing through excess vocabulary) | https://arxiv.org/html/2406.07016v1 | 2024 | preprint (published in Science Advances 2025)
       Studied, high-confidence. 14.2M PubMed abstracts; ~280 excess words in 2024, 66% verbs / 18% adjectives; delves r~25, showcasing r~9, underscores r~9; high-gap common words potential/findings/crucial;
  SRC: Why Does ChatGPT 'Delve' So Much? Exploring the Sources of Lexical Overrepresentation in Large Language Models | https://arxiv.org/abs/2412.11385 | 2024 | preprint (Juzek & Ward, Florida State University)
       Studied. Names the canonical 21 focal words (delve, intricate, commendable, meticulous, surpass, elevate, foster, tapestry, realm, navigate, landscape, pivotal, resonate, testament, underscore, showca
  SRC: Delving Into PubMed Records: How AI-Influenced Vocabulary has Transformed Medical Writing since ChatGPT | https://pmejournal.org/articles/10.5334/pme.1929 | 2025 | peer-reviewed paper (Matsui, Perspectives on Medical Education 14(1):882-890)
       Studied. Modified Z-score ranking of 2024 PubMed terms: top movers delve, underscore, primarily, meticulous, boast, commendable, showcase, surpass; 103 terms with Z>=3.5; notes some trends began ~2020
  SRC: Why Does ChatGPT 'Delve' So Much? FSU researchers begin to uncover why ChatGPT overuses certain words | https://news.fsu.edu/news/science-technology/2025/02/17/why-does-chatgpt-delve-so-much-fsu-researchers-begin-to-uncover-why-chatgpt-overuses-certain-words/ | 2025 | university news / research summary
       Studied (secondary). Source for the delve+underscore 98.8% co-occurrence figure and the delve+realm+underscore triple appearing only in 2023-2024 across PubMed records back to 1849; frames the 21-foca
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SUMMARY: test summary of sufficient length to satisfy the schema requirements here.

* Test tell
  WHAT: a test pattern
  FIX:  a test fix

  SRC: Test | https://example.com |  | 
       test
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SOURCE VERIFICATION: 13 checked, 1 flagged unreal
  UNREAL: Test | https://example.com | example.com is reachable but is an IANA placeholder domain, not a genuine source; entry ('Test'/'test') is filler, not a real reference.