Why LLMs Are Missing the Mark on Language Diversity

Language models have come a long way, but they're not without their flaws. The press release said AI transformation. The employee survey said otherwise. reshaping linguistic features, these models are doing more harm than good.

Language Models and Their Linguistic Woes

Let's face it, large language models (LLMs) are reshaping how we understand linguistic features. They aren't just stylistic tools, they're becoming probes of probability mass. With modern training pipelines, we're seeing a drastic re-distribution of language. So, what's happening? Instruction-tuned systems are collapsing language entropy. They're tuning down complex punctuation to just 3.2-23.2% of baseline frequencies. It's like teaching a chef to cook without spices.

What the Numbers Tell Us

The numbers don't lie. We're talking about a mean amplification of 1,949-16,853%, with peaks reaching 209,675%. Weak interventions only worsen this collapse by 240%. In contrast, strong control measures (lambda=5.0) improve outcomes by 40.5% and outperform frontier models by up to 98.2%, even when they're dwarfed by 200-1000x in scale. That's like David smiting Goliath, not with a rock, but with well-placed linguistic control.

A Call for Stronger Control

It's clear that we're not just missing the mark, we're entirely off the field. Modern alignment pipelines need a serious overhaul. Simply smoothing out distributions isn't cutting it. When lambda=5.0 brings 15% higher distinct-4, 27% higher vocabulary diversity, and 78% lower repetition, it's time to reconsider our approach. So, why are we still relying on outdated metrics?

Here's what the internal Slack channel really looks like: models are failing to align language properly, causing potential issues like AI detection problems, data contamination, and even long-term effects on linguistic evolution. We're watching language as we know it, reshaped by invisible forces in training pipelines.

The Crux of the Matter

So, where does this leave us? With a pressing need to adjust how we train and measure these models. Management bought the licenses. Nobody told the team. Because if we're not careful, we might just end up with language models that speak more like robots than humans. And that's a future nobody wants.