alt Hacker NewsLots of people have their own voice and tend to prefer certain phrases. This has been the case for a long time and is generally not a big issue.
Now LLMs come along and they also have their own phrasing preferences. But now it's a problem because what used to be personal preferences of a single person that manifests in 5000 words per day from one person tops, is now the bias of a single model multiplied x10,000,000,000 generated tokens per day so any bias sticks out like a sore thumb.
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> But now it's a problem because what used to be personal preferences of a single person that manifests in 5000 words per day from one person tops, is now the bias of a single model multiplied x10,000,000,000 generated tokens per day so any bias sticks out like a sore thumb.
I am more pessimistic than that. Soon enough even people will start talking like LLMs. After listening to 5000 words per day, especially growing up, getting "help" with the homework, kids will start talking like LLMs.
- "Did you eat the cookies, Jimmy?"
- "You're absolutely right to question me, father. In fact I did eat all the cookies. But it's not a load-bearing issue. My honest take is we can go to the store and buy more".
It's not just "certain phrases". It's the entire structure of the writing — the idioms, the small-scale grammatical patterns, and the strangely inapt similes that, despite making semantic sense, nevertheless manage to blindside human readers like a foreign object in their peripheral vision.
(This is intentional parody. Please don't shoot me.)
An interesting solution would be for these AI companies to train a few different versions of these models, all with different speech characteristics. Then, when you start a conversation, you get a random version.
This affinity for verbal tics, too, seems learned from humans...
See, for example, "synergy", "proactive", "in the loop," and hundreds more that proliferate in corporate jargon with even more senselessness than the LLMs.
I hate all this "smoke", personally. Smoke tests, smoking guns, ...
If you put important Anthropic blog posts like the Fable announcement or J-Space through Pangram, you get 100% human written. Considering that the overwhelming majority of the code there is written by AI, I think this is an admission that AI writing is slop and AI code is pretty good.
Yeah wow fascinating! It's almost like LLM output quality was never the point from a business perspective.
Real people think in concepts and experiences instead of words. The words are not so important to get the idea across, but LLMs only model language.
The problem is fundamental. There's no workaround. Averaging out word usage might even make the problem worse.
It's not the voice. It's the repetition.
/s
I think it might be even worse. LLMs seem to get tragically stuck on certain patterns. Maybe it's partly because a pile of weights essentially always starts from scratch in the same condition, but even within a single conversation, it will literally just latch onto words and repeat them incessantly, to the point where it becomes annoying.
So for example, current Claude models love "honest". They are always producing "honest" assessments. "The honest caveat" - I'm sorry, did you mean the caveat, period? But also, use the wrong phrasing and suddenly you can create your own word of the day for an AI model. I used the word "analytical" once, in a conversation with Gemini 3 Pro. I am pretty sure every single response from that point on had "analytical" in it at least once.
This is especially funny because system prompts and whatnot can also cause this behavior, but at least you can tweak those. You can't really do much about the model weights just having a weird affinity for a word.
I bet someone will or probably already has come up with a way to detect and prevent these problems during training or post training. I'm not saying it's an easy problem, but it has the benefit that it really should be detectable with just statistics.