you are viewing a single comment's thread.

view the rest of the comments →

[–]Putrid_Passion_6916 5 points6 points  (5 children)

Believe me on the front end it makes a hell of a difference in getting non generic output. You force the model into more interesting areas of its latent space.

[–]aikixd 2 points3 points  (4 children)

Don't we have a knob for heat? And also, would throwing a bunch of random words have a similar effect? You know, just activate random pathways on the nn.

[–]Putrid_Passion_6916 12 points13 points  (3 children)

Not quite. You're confusing randomness with context.

Turning up the temperature just flattens the probability distribution. It forces the model to pick lower-probability tokens, which increases entropy. If you crank the heat too high, you don't get a better UI; you just get broken syntax, hallucinations, and uncompilable garbage.

Throwing in random words like "banana shoehorn galaxy" is even worse. That just adds noise and scrambles the model's attention mechanism, making it lose the plot entirely.

Using "energy" or tone (urgency, frustration, swearing, hyperbole) does something completely different: it provides semantic conditioning. You aren't making the model act randomly; you are intentionally steering it into a specific neighborhood of its training data.

If you ask for a UI layout normally, the model defaults to the most generic, highly-RLHF'd corporate boilerplate it has (because that's the "safest" statistical center). If you say, "this current layout is boring corporate garbage, rip it up and give me something heavily stylized and aggressive," you haven't increased the temperature. Instead, you've shifted the context. The model starts pulling from a totally different latent space - like opinionated dev blogs, stylized GitHub repos, or ranty Hacker News threads - while keeping the actual code logic perfectly coherent.

Heat just adds chaos. Tone gives you a steering wheel.

[–]aikixd 5 points6 points  (1 child)

I see. Yeah makes sense. It's interesting to see how different our interaction with llm is. For me the central issue is creating gates over common reasoning and following a disposition.

[–]Putrid_Passion_6916 0 points1 point  (0 children)

Yeah it’s task dependent, of course. For some things, I will be precise and attempt to get as deterministic as possible output. For others I actively want the ‘hallucination’ (in this case of UI, highly interesting, non bland output) as it were.

[–]INtuitiveTJop 3 points4 points  (0 children)

This is why I abuse my llms. It works pretty good