Typical AI Words and Phrases Used by LLMs

Table of Contents

• Do large language models have their own vocabulary?
• Why LLMs Have Typical Word Choices
• Common Words Overused by LLMs
• Stylistic Tendencies Behind Typical Words
• Implications for Detecting AI Text
• Limitations & Evolving Trends
• Conclusion
• FAQ

Do large language models have their own vocabulary?

Large Language Models, for example ChatGPT, Claude, along with Gemini, became standard tools for making text that sounds like a person wrote it. However, the words they use have specific patterns and choices that point to the text's artificial origin. Understanding these typical words helps with figuring out if content came from an AI and also helps with understanding how LLMs use language.

Why LLMs Have Typical Word Choices

LLMs receive training using large collections of books, articles, websites, as well as code. They learn the chance that some words will follow others in specific situations. They do not learn real meaning. This system leads to particular words and phrases appearing more often in AI work than in human work.

Because LLMs create answers that fit the situation based on learned probabilities, they often choose formal or neutral language. This can make them repeat certain words or phrases. Those words fit many situations but might sound too proper or robotic.

Common Words Overused by LLMs

Studies located several words that appear more often in AI writing than in human writing. These words include:

• Aligns - AIs use this word sixteen times more often than people. The word suggests business writing, which appears often in training data.
• Aims to explore - Phrases using "aims to" show up about fifty times more often in AI writing. This structure introduces topics in a polite way, but humans rarely use it so often.
• Today’s fast-paced world - This common phrase appears one hundred seven times more often in AI work. It attempts to sound current, but is too general.
• Notable works include / Notable figures - These formal terms appear more than one hundred twenty times more often in AI-made text than in human-made text.
• Surpassing - This word appears twelve times more often. Interestingly, synonyms like “exceeding” do not show the same preference by AIs.
• Tragically - AIs use this emotional word eleven times more often. The reason for this is not clear. It could be because of training examples containing dramatic stories.
• Impacting / Making an impact - These phrases have about eleven times more frequency compared to people. Similar terms like “affecting” are less often used by AIs, despite having a close meaning.

Other phrases show up often in LLM work:

• “Research needed to understand”
• “Despite facing”
• “Expressed excitement”
• “Evolving situation”

These phrases sound like formal academic papers or news reports. These writing types appear a lot in the training data, but less so in casual talk or human creative writing.

Stylistic Tendencies Behind Typical Words

The preference for some words appears partly because LLMs try to be clear and neutral across a range of subjects. They avoid informal talk or sayings, unless told to use them, because those forms change more and have less predictability.

Words like *delve*, *realm*, *evolving situation* show a preference for slightly fancy language. That language appears often in scientific summaries, reports, next to learning materials. These sources take up a large part of the training data. The repeating use of these words makes a pattern that shows machine work, not natural human writing.

Also, some preferred terms work as safety measures ("aims to explore," "research needed"). They allow the model to stay flexible without making a promise. This is useful when the model is not completely sure because it does not truly understand the information.

Implications for Detecting AI Text

The overuse of typical words offers clues for finding out if an LLM made the content, instead of a person. Tools look at how often words appear compared to the standard for human writing. They point out cases where such markers show up too often.

For example:

Word/Phrase	Frequency Increase (AI vs Human)
Aligns	~16x
Aims To Explore	~50x
Today’s Fast-Paced World	~107x
Notable Works Include	>120x
Surpassing	~12x
Tragically	~11x
Impacting	~11x

This difference helps teachers, editors, journalists, in addition to readers to flag passages that a machine may have written. Those passages require closer inspection.

Limitations & Evolving Trends

The lists of typical words offer helpful clues now, but are not perfect for always finding AI writing. As models get better through training on conversations or with feedback that focuses on naturalness over properness, their word use may change from present habits.

Also:

• Some human authors use formal, academic language.
• The situation matters. A scientific paper naturally has flagged terms.

Finding AI must look at overall writing features beyond just individual words.

Conclusion

Typical AI words used by large language models show how they work. They recognize patterns across many text sources. This makes them choose vocabulary marked by properness and careful phrasing. Words like *aligns*, *aims to explore*, *notable works include*, along with common phrases like *today’s fast-paced world* take up much of the made text. This happens mainly because of probability, not need for a specific meaning.

Noticing these language patterns helps show when automated content exists. It also provides understanding of how artificial intelligence forms current writing styles. That influence will likely keep developing.

FAQ