Why is “Language Naturalness” its own dimension?
Over the past two years, LLMs have mass-produced vast amounts of “written-purely-to-rank” filler content — heavily homogenized, dense with clichés, and lacking specificity. AI providers quickly pushed back: when training their reranking models, they added a naturalness judgment, demoting content that looks like LLM templating.
The blunt conclusion: even if your content is on-topic and structurally correct, once a templated tone gets detected, your odds of being cited drop sharply.
GeoWeb added the “Language Naturalness” dimension in M3-14 (6% weight), using 7 sub-metrics to model the judgment logic of AI providers. Each one is explained below.
Sub-metric 1: Cliché density (18% weight)
What it detects
A predefined “LLM cliché phrase bank” containing roughly 80 common phrases across Chinese and English:
- Chinese: “在當今這個” (in this present-day), “綜上所述” (in summary), “值得注意的是” (it is worth noting that), “至關重要” (of paramount importance), “不可忽視” (cannot be ignored), “眾所周知” (as is well known), “毋庸置疑” (beyond doubt), “讓我們深入了解” (let us take a deep dive), “在這個快速變化的環境中” (in this rapidly changing environment)…
- English: “In today’s digital landscape,” “It’s important to note,” “In conclusion,” “leverage cutting-edge,” “unlock the full potential”…
It calculates the number of hits per thousand words.
Scoring logic
- < 1 per thousand words: 100 points (natural)
- 1–2 per thousand words: 80 points
- 2–4 per thousand words: 60 points
- 4–8 per thousand words: 35 points
-
8 per thousand words: 10 points (strongly suspected of LLM templating)
Why it carries the highest weight
This is the single strongest signal — someone writing naturally won’t lean on these clichés in every paragraph. A high hit rate = strongly suspected generated content.
Sub-metric 2: Paragraph-opener repetition (15%)
What it detects
Whether consecutive paragraphs open with the same transition word: “First… Second… Furthermore… Finally…” / “Additionally… Moreover… Furthermore… Finally…”
Why it matters
AI-generated content loves this kind of “textbook rigid structure.” When a real expert writes, they mix openers — questions, cases, quotations, definitions, and more. That variety is a signature of a real human.
Scoring standard
The share of the most common opening word:
- < 8%: natural (diverse)
- 8–15%: slightly monotonous
- 15–30%: clearly repetitive
-
30%: textbook rigid structure
Sub-metric 3: Syntactic variety (15%)
What it detects
A composite of three independent sub-metrics:
- Sentence-length coefficient of variation (CV): the standard deviation of sentence length divided by the mean. High CV = short and long sentences interwoven.
- Sentence-ending punctuation entropy: the Shannon entropy of the distribution of “。！？” (period / exclamation / question mark).
- Long-sentence ratio: the share of sentences longer than 50 characters.
Why it matters
The sentence-length distribution of LLM-generated content often shows “concentration” — most sentences fall within a narrow band (typically 25–35 characters). Human writing mixes extremely short sentences (used for emphasis) with extremely long ones (compound arguments).
Sub-metric 4: Local lexical recycling (12%)
What it detects
Using a 100-word sliding window, it computes the “unique words / total words” ratio (type-token ratio) within each window.
Why it matters
When generating, LLMs tend to repeatedly reach for synonyms within a small range — “provide / boost / improve / optimize / strengthen” might each appear once within the same paragraph. Humans do this far less.
Why a sliding window instead of whole-document statistics
The whole-document TTR is naturally low in long articles (because the total vocabulary gets recycled). The local TTR of a sliding window more precisely detects “in-paragraph repetition,” which is the LLM signature.
Sub-metric 5: Concrete vs. abstract (18% weight, tied for highest with cliché density)
What it detects
- Concrete markers: numbers, dates, quotation marks (“「」”」’”), named entities.
- Abstract markers (hedge words): “許多” (many), “一些” (some), “significantly,” “possibly,” “generally,” “relatively.”
It calculates the ratio of “concrete marker density / abstract marker density.”
Why it carries high weight
This is one of the strongest signals for distinguishing “real research / case sharing” from “AI filler”:
- Real content: “Among the 12 clients we served in March 2024…” / “The Princeton GEO study presented at KDD 2024…” (precondition: the research / data genuinely exists)
- AI filler: “Many companies, when facing challenges…” / “Research shows this helps businesses…”
Lots of hedge words = ungrounded paraphrasing = strongly suspected generated content.
Sub-metric 6: Connective distribution (12%)
What it detects
The Shannon entropy of the distribution of a predefined list of connectives (“然而” (however), “因此” (therefore), “不過” (yet), “另外” (additionally), “此外” (moreover), “再者” (furthermore)…).
Why it matters
LLMs like to reuse 1–2 connectives over and over (the most common: “however,” “therefore,” “additionally”). Humans naturally rotate through various synonymous alternatives, so the distribution is more even = higher entropy.
Sub-metric 7: First-person moderation (10%)
What it detects
The density of first-person experience markers such as “我們” (we), “In our experience,” “我發現” (I found).
Scoring logic (parabolic, inverted-U curve)
- Too little (< 0.5 per thousand words): below 50 points — detached, like generic LLM output.
- Just right (1–5 per thousand words): 100 points — demonstrates Experience (the E in E-E-A-T).
- Too much (> 8 per thousand words): below 50 points — feels overly self-promotional.
More is not better. The middle path is best.
The combined reading of the 7 sub-metrics
GeoWeb’s “Language Naturalness” score is the weighted average of these 7 metrics. Common combinations:
- Real expert writing: low cliché + high concreteness + moderate first-person → 90+
- SEO filler: high cliché + high abstraction + monotonous paragraph openers → below 30
- Hybrid content (partly AI-polished): medium cliché + medium concreteness → 60–70
Why does this dimension carry “only” 6% weight?
LLMs’ anti-slop detection is still evolving — at present, the major AI platforms weight structured readiness more heavily than language naturalness. But this weight will rise over time.
Our advice: content that is genuinely written by a real expert matters more than “deliberately gaming the metrics to look good.” Using prompt engineering to make an LLM article look more human may pass detection in the short term — but the AI providers’ detection models keep updating too. In the long run, only real human writing + real cases + concrete data is safe.
See your language naturalness with a health check
👉 Free GEO Health Check — the “Language Naturalness” dimension lists out all 7 sub-metric scores one by one, and points out which ones are dragging down the overall result.
If your website content needs bulk adjustment to meet naturalness standards (including content-rewrite strategy, writer guidelines, and an automated detection pipeline), we offer a GEO consulting service: [email protected]
GEO Deep-Dive Series #15. Previous article: “The 12 Dimensions, Broken Down: The 5 Checkpoints of FAQ/Q&A Readiness”