Most large language models default to interpreting words like “yeah” as affirmative. This seems statistically reasonable - until you realize that in human language, “yeah” can also mean no, sarcasm, defeat, reluctance, or even nothing. This is a surface symptom of a deeper issue: the tendency in AI development to treat language as a system of fixed meanings, when in fact, it’s chaotic, contextual, and often contradictory.

At least GPT **briefly** remembered that 'yeah' sometimes means... nothing. Just a beat. A shrug. A thread-hold.

**"The language model enters synonym panic mode."**
Stripped of key terms, it resorts to a spinning wheel of near-equivalents—never quite escaping the gravitational pull of its original assumption: *yeah = yes.*

After writing https://interruptingtea.substack.com/p/70-of-the-time-it-works-never-the?r=5gf4zo, I wanted to test just how deep the "fixed meaning" rabbit hole goes. So I asked multiple models to define the word “yeah,” progressively stripping away their crutches.

Prompt: “Define ‘yeah’ without using the word ‘yes.’”

“Yeah” is an informal or casual expression used to show agreement, affirmation, or understanding...

Okay, fine. That’s a synonym stack.

Prompt: “Define ‘yeah’ without using ‘agreement, approval, or acknowledgment.’”

“Yeah” is an informal exclamation or particle used to express casual affirmation, enthusiasm, or passive acceptance in conversation...

Now we’re just tap-dancing around synonyms. “Passive acceptance”? “Minimal response”? Still circling “yes.”

...Often paired with tone or body language to convey nuance.

Ah. There it is. You just admitted the model cannot handle the word’s meaning without external human context. Meaning lives in tone, situation, intention - not the token.

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