Why Do LLMs Insert Random Hyphens in Sentences? Decoding AI's Writing Quirks
Ever been reading something online & it just felt… off? Maybe the grammar was a little too perfect, the sentences a bit too uniform. Or maybe you noticed it—the tell-tale sign, the punctuation mark that’s become the unofficial signature of AI writing.
I’m talking about the em dash.
And not just the em dash, but its weirder, more chaotic cousin: the randomly inserted hyphen. You’ve seen it. A word like “abso-lutely” or “in-correct” sitting there in the middle of a sentence, making you wonder if the author had a momentary spasm while typing.
Turns out, there’s a fascinating reason behind both of these quirks. It’s not a bug, not really. It’s a window into how these massive, complex Large Language Models (LLMs) actually “think” about language. It's a story about data, probability, & the weird little artifacts that get left behind when you teach a machine to write like a human.
So, let's dive in. We’ll unpack the whole em-dash-versus-hyphen mystery, get into the nitty-gritty of why it happens, & explore what it means for how we interact with AI.
The Em Dash Dilemma: The Punctuation Mark of the Machines
First, let's talk about the big one—the em dash (—). It’s that long dash that writers use for emphasis, to set apart a clause, or to create a dramatic pause. And honestly, AI LOVES it. To an almost comical degree.
If you’ve spent any time on the internet lately, you’ve probably seen posts where nearly every other sentence is broken up by an em dash. It’s become such a recognized “tell” that people on Reddit & Hacker News are constantly calling it out. Some have even dubbed it the "ChatGPT hyphen."
So, why the obsession? It boils down to a few key things.
It’s All in the Training Data
Here’s the thing about LLMs: they don’t know anything. They are prediction engines. They’ve been trained on a truly mind-boggling amount of text from the internet—books, articles, scientific papers, blogs, you name it. They learn by identifying patterns.
And what kind of writing do you think makes up a huge chunk of that high-quality training data? Professionally written & edited content. Books by seasoned authors, articles from major publications, essays from academics. And guess what punctuation mark those folks love to use for stylistic flair? The em dash.
The AI sees that respected writers use em dashes to sound authoritative & create a certain rhythm. So, it learns a simple lesson: "To write high-quality text, I should probably use a lot of these em dashes." It’s not making a conscious choice; it’s just replicating the patterns it was trained on. It’s trying to be a good student, but it ends up overdoing it, like a kid who just learned a new word & uses it in every sentence.
No Keyboard, No Problem
For us humans, typing an em dash is kind of a pain. On a Mac, it's Option+Shift+Hyphen. On Windows, it’s Alt+0151. It’s not exactly convenient. Most people just type two hyphens (
) & let their word processor auto-correct it. In casual writing, most of us don't bother at all & just use a regular hyphen.
But an AI doesn’t have fingers. It doesn’t use a keyboard. For an LLM, generating an em dash is just as easy as generating the letter 'a'. There's no extra friction. So, while a human writer might subconsciously use them sparingly because they’re a hassle to type, the AI has no such limitations. It sees the pattern in its data & just goes for it, every single time it seems appropriate.
The Rise of the "AI-Dar"
This whole phenomenon has had a pretty funny side effect. People have gotten so good at spotting the "AI dash" that human writers are now becoming afraid to use it. Writers who have been using em dashes for decades are suddenly finding their work flagged as AI-generated. It's a bizarre new form of digital profiling, where a perfectly valid punctuation mark has become a scarlet letter for robotic writing.
This is a HUGE problem for businesses trying to use AI for content creation or customer service. The last thing you want is for your company blog or your support bot to scream "I'M A ROBOT!" because it’s littering every response with em dashes. You need a voice that’s consistent, natural, & distinctly yours.
This is actually where a tool like Arsturn comes in handy. Instead of relying on a generic model that’s been trained on the entire internet (with all its weird quirks), you can build a no-code AI chatbot trained specifically on your own data. You feed it your company’s internal documents, your past customer conversations, your brand style guide—everything that defines your voice. The result is a chatbot that talks like you, not like a generic AI trying to sound smart. It can provide instant, 24/7 support while staying perfectly on-brand, avoiding those tell-tale signs like em dash overuse.
The Mystery of the Misplaced Hyphen: A Deeper Glitch in the Matrix
Okay, so the em dash thing is mostly a stylistic quirk. But what about the other issue? The one that’s less about style & more about just being… wrong?
I'm talking about things like this:
- "That's a really innov-ative solution."
- "Please don't mis-understand my point."
- "The process is fairly straight-forward."
This isn’t an em dash. This is a simple hyphen, stuck where it doesn’t belong. This isn't the AI trying to be fancy. This is a much deeper & more interesting artifact of how LLMs actually work on a fundamental level. To understand it, we need to talk about something called tokenization.
LLMs Don’t Read Words—They Read "Tokens"
This is the most important concept to grasp: LLMs don't see words, sentences, or paragraphs the way we do. They see tokens.
Before an LLM can process your prompt, it has to break it down into smaller pieces. This process is called tokenization. A token can be a whole word, a part of a word (a subword), or even just a single character. For example, the word "unbelievable" is a single word to us. But to an LLM, it might be broken down into three tokens:
,
, &
. The word "tokenization" might become two tokens:
&
.
This is done using clever algorithms like Byte-Pair Encoding (BPE). The algorithm scans a massive amount of text & learns the most common combinations of characters. It builds a vocabulary of these character chunks, or tokens. This is SUPER efficient. Instead of needing a vocabulary of millions of words (including every possible variation like plural, past tense, etc.), the model can have a much smaller vocabulary of a few thousand tokens & still be able to construct almost any word. It also allows the model to handle words it’s never seen before by breaking them down into familiar subword parts.
It’s a brilliant system. But it’s also the root cause of our random hyphen problem.
Where the Tokenization Process Breaks Down
The tokenization process is invisible to us, but it's the source of a lot of "LLM weirdness." The random hyphen is a classic example of what happens when this process hits a snag.
Here’s how it can happen:
Out-of-Vocabulary Mishaps: Let's say you give an LLM a weird or complex word that isn’t in its pre-defined token vocabulary. What does it do? It panics—metaphorically, of course. It has to break that word down into the smaller tokens it does know. Sometimes, this process is messy. The model might grab a token that includes a hyphen, or it might get confused about the word boundary & insert a hyphen where it thinks a split should occur. It's like a linguistic emergency surgery, & the hyphen is the stitch.
Pattern Over-application: The model learns from patterns, but sometimes it learns them TOO well. For example, it sees hyphenated compound words like "state-of-the-art" or "well-being" thousands of times in its training data. It develops a strong statistical association between certain subwords being joined by a hyphen. Then, it might see a word that starts with "well" but isn't "well-being" & still have a high probability of wanting to insert a hyphen after it, leading to an error like "well-done" being written as "well- done" or something even stranger.
Sensitivity to Noise: LLMs can be surprisingly fragile. A single typo or an unusual character can completely change how a word is tokenized. This can throw the model off balance & cause it to generate strange outputs, including misplaced hyphens, as it tries to make sense of the mangled tokens. It's trying to find the most probable next token, but the initial input has led it down a weird, low-probability path.
So, when you see a word like "abso-lutely," you're not seeing a typo in the traditional sense. You're seeing the ghost of the tokenization process. You're seeing the seams where the LLM stitched together different subword units to form a complete word, & for whatever statistical reason, a hyphen got caught in the crossfire. It's a fascinating peek under the hood at the messy, probabilistic way these models handle the beautiful, complex system we call language.
Why This Matters for Your Business
This kind of error is more than just a funny quirk. If you're a business using an LLM for anything customer-facing, it's a real problem. Imagine a customer interacting with your website’s chatbot to get help with a product. If the bot starts spitting out sentences with random, misplaced hyphens, it immediately shatters the illusion of professionalism & competence.
It erodes trust. It makes your company look sloppy & technologically inept. Customers might wonder, "If they can't even get the chatbot to write correctly, can I trust their product or their security?"
For business-critical applications like lead generation, customer engagement, & automated support, you can't afford these kinds of unforced errors. You need an AI solution that is robust, reliable, & trained for your specific needs. This is where a dedicated conversational AI platform like Arsturn becomes so important. By building a no-code AI chatbot that's trained on your business data, you create a much more controlled environment. The model is optimized for your vocabulary, your product names, & your way of speaking. This significantly reduces the chances of weird tokenization artifacts & ensures your AI assistant provides clear, accurate, & professional responses every single time. It helps you build meaningful connections with your audience through personalized chatbots that actually work, without the embarrassing quirks of a general-purpose model.
So, What's the Takeaway?
The strange case of the AI hyphen—both the overused em dash & the misplaced hyphen—is more than just a trivia fact. It's a perfect illustration of both the power & the current limitations of large language models.
They are incredible pattern-matching machines that can replicate the style of vast swathes of human writing. But they don't understand it. They don't know why an em dash adds a certain flair, or why "innovative" isn't spelled with a hyphen. They're just playing a very, VERY advanced game of statistical probability.
As we integrate AI more deeply into our lives & businesses, being aware of these quirks is crucial. It helps us be better consumers of AI-generated content, & it pushes us to demand better, more refined tools for professional use cases. The goal isn't just to create content that's grammatically correct, but to create interactions that feel natural, authentic, & human. And sometimes, that means knowing when not to use a dash.
Hope this was helpful & shed some light on this weird little corner of the AI world. Let me know what you think! Have you seen any other strange AI writing quirks in the wild?