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EMIRELADEROtoday at 6:24 PM3 repliesview on HN

The former, because it's anthropomorphizing a model.

Anthropic is especially guilty of this. They have been using such language for a while, like when they analyze model weights for mechanistic interpretability and call it the model's "biology".

It's just distasteful.


Replies

godwinson__4-8today at 6:33 PM

> The former, because it's anthropomorphizing a model.

Not really. The input and output is already natural language. That is already "anthropomorphizing".

That is, if this is the bar for anthropomorphization its already happened.

Telling the model to "believe in itself" is just stochastic manipulation that has shown enough reliability to be a recipe to make it keep going.

It's only actually anthropomorphizing if you forget it's a trick and think it's a real person.

There is nothing distasteful about it. If people get confused that's on them. They wouldn't be very useful if you couldn't just talk to them. That's kind of the whole point. Otherwise you can just go back to coding by hand. Telling it to believe itself is just input that happens to work. This probably tells us more about human nature than you realize given the corpus on which it is trained. It obviously doesn't mean anyone actually thinks it's a person.

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NitpickLawyertoday at 6:48 PM

> because it's anthropomorphizing a model.

Is it though? There's a perfectly "technical" reason why this strategy should work, without any sort of anthropomorphising:

Assume models are trained on vast amounts of data. Assume that the model is asked to solve something that the literature says it's impossible. It will start generating tokens towards that "this is a famous conjecture, it's not possible to prove it, blah blah". Assume the model was also trained on books/novels/etc. Assume the model was also also trained on "solving" many math problems. Now, you can make an argument that just placing "you can do it" in the context will "steer" the model towards generating "moving forward" tokens. Take ideas, generate tokens, go towards negative. "You can do it". Model starts generating tokens again, more ideas, more "exploration". More negativity. "I believe in you keep going". The two (book tropes + math CoT) mix together in the context. The model keeps on "pushing" and "vibing" between the two. Ta dah, it works.

mannycalavera42today at 6:42 PM

> The former, because it's anthropomorphizing a model.

The Yegge thinks differently https://yegge.ai/essays/model-welfare/

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