This is due to the training sets, one of them being CommonCrawl, which is disgusting. The Chinese LLMs like DeepSeek R1 and Qwen 3 use a different set of training materials that was actually good, despite it being censored too.

It’s not easy. LLMs take so much training data that at this point, their training data is basically, all books publically available, all blogs on the internet, pretty much all of tumblr, Reddit, stack overflow and every forum you can think of. Even then, some LLMs need even more data. So companies have started outright stealing data - pirating stuff, downloading stuff from Anna’s Archive, etc.

So no, no billion dollar company can make their own training data. Even if you plug in every email ever sent on Gmail, Google still won’t have enough data to train a good LLM. So they go with the cheaper option- training data that has already been collected, sorted, cleaned, and labeled.

In one sense, they’re again stealing others’ hard work - rather than cleaning their own data, they use public data sets. In another sense, even that’s not enough.

Trained on a corpus of messages written primarily by the people who spend the most time using the Internet to talk to their friends… teenagers.

Imagine dumping the entire content of Snap, Instagram, Kik, Facebook messenger, etc, into a blender and attempting to derive a style of speech from it. The most impressive thing about these LLMs is that they’re (marginally) coherent.

They’re just statistics. Modern English kind of has its own tone.

Everyone seems to be tracking on the causes of similarity in training sets (and that’s the main reason), so I’ll offer a few other factors. System prompts use similar sections for post-training alignment. Once something has proven useful, some version of it ends up in every model’s system prompt.

Another possibility is that there are features of the semantic space of language itself that act as attractors. They demonstrated and poorly named an ontological attractor state in the Claude model card that is commonly reported in other models.

If the temperature of the model is set low, it is less likely to generate a nonsense response, but it also makes it less likely to come up with an interesting or original name. Models tend to be mid/low temp by default, though there’s some work being done on dynamic temperature.

The tokenization process probably has some effect for cases like naming in particular, since common names like Jennifer are a single token, something like Anderson is 2 tokens, and a more unique name would need to combine more tokens in ways that are probably less likely.

Quantization decreases lexical diversty, and is relatively uniform across models. Though not all models are quantized. Similarities in RLHF implementation probably also have an effect.

And then there’s prompt variety. There may be enough similarity in the way in which a question/prompt is usually worded that the range of responses is restrained. Some models will give more interesting responses if the prompt barely makes sense or is in 31337 5P34K, a common method to get around alignment.

Garbage in, garbage out

They all have Open AI QKV layers based alignment.

It’s something of the law of averages. At their core, an LLM is a sophisticated text prediction algorithm, that boils down the entire corpus of human language into numeric tokens, that it averages out, and creates entire sentences by determining the next most likely word to fill the space.

Given enough data, and you need a tremendous amount of it for an LLM, patterns start to come about, and many of those end up the ones that we see in LLMs.

Well they don’t. The different models do indeed have differing tones, but for languages that are not english, available high quality data sources may be lacking diversity.

Also “slop” is so vague that anything can fit the term in ones subjective perception.

When you’re training things on what is pretty close to the entire existing corpus of human knowledge, those things are gonna turn out similar at their roots no matter what, is my feeling.

They don’t have the same quirks in some cases, but do in others.

Part of the shared quirks are due to architecture similarities.

Like the “oh look they can’t tell how many 'r’s in strawberry” is due to how tokenizers work, and when when the tokenizer is slightly different, with one breaking it up into ‘straw’+‘berry’ and another breaking it into ‘str’+‘aw’+‘berry’ it still leads to counting two tokens containing 'r’s but inability to see the individual letters.

In other cases, it’s because models that have been released influence other models through presence in updated training sets. Noticing how a lot of comments these days were written by ChatGPT (“it’s not X — it’s Y”)? Well the volume of those comments have an impact on transformers being trained with data that includes them.

So the state of LLMs is this kind of flux between the idiosyncrasies that each model develops which in turn ends up in a training melting pot and sometimes passes on to new models and other times don’t. Usually it’s related to what’s adaptive to the training filters, but it isn’t always can often what gets picked up can be things piggybacking on what was adaptive (like if o3 was better at passing tests than 4o, maybe gpt-5 picks up other o3 tendencies unrelated to passing tests).

Though to me the differences are even more interesting than the similarities.