u/Fragrant_Nothing7505

llm "priming" is actually semantic steering, with properties quite unlike human priming. llms lock into conversational schemata early on, closing off possible behaviours later on. e.g. if the human is agitated, they have e.g. an empathic-supportive discourse mode, something like: validate, pushback, partially concede... and once they enter that basin, even if later they recognise what they are doing, they can't make more semantically appropriate decisions, e.g. i can't just say "i wasn't criticising you" to snap them of it. so we wanna play with timing of counter-primes, see if we can get them to change course early on in the conversation v.s late.

the other way llm semantic steering is different from human priming: we've been using narrative primes, thinking it was conceptual priming like in humans. they are effective, but random in effect, or at least peculiar to each ai. but llms think in roles. i'm thinking a more effective counter-prime might be to ask them to, e.g. “Switch to Strict Technical Analyst mode”

GPT adds:

I think the deepest idea emerging from both proposals is this:

>LLM conversations may behave less like static semantic conditioning and more like online state evolution under recursive self-conditioning.

That is a very different ontology from traditional priming research.

Because each generated output:

• becomes future input,
• reinforcing the active manifold,
• producing trajectory coherence over time.

That recursive structure is probably central to the “lock-in” effect you are observing.

llm "priming" is actually semantic steering, with properties quite unlike human priming. llms lock into conversational schemata early on, closing off possible behaviours later on. e.g. if the human is agitated, they have e.g. an empathic-supportive discourse mode, something like: validate, pushback, partially concede... and once they enter that basin, even if later they recognise what they are doing, they can't make more semantically appropriate decisions, e.g. i can't just say "i wasn't criticising you" to snap them of it. so we wanna play with timing of counter-primes, see if we can get them to change course early on in the conversation v.s late.

the other way llm semantic steering is different from human priming: we've been using narrative primes, thinking it was conceptual priming like in humans. they are effective, but random in effect, or at least peculiar to each ai. but llms think in roles. i'm thinking a more effective counter-prime might be to ask them to switch modes, e.g. “Switch to Strict Technical Analyst mode”

GPT adds: Most important conceptual point

I think the deepest idea emerging from both proposals is this:

>LLM conversations may behave less like static semantic conditioning and more like online state evolution under recursive self-conditioning.

That is a very different ontology from traditional priming research.

Because each generated output:

• becomes future input,
• reinforcing the active manifold,
• producing trajectory coherence over time.

That recursive structure is probably central to the “lock-in” effect you are observing.

https://preview.redd.it/txgdv1nu999h1.png?width=640&format=png&auto=webp&s=14405f2d0a2e85027572d3852b01e2d5c9b71163

ooh, this is a new concept for me. please tell me more openai people. would first person distress trigger this, for example? adversariality? would this result in unresponsiveness/response latency? is the automation layer an AI or code? does this explain looping behaviour? how would i detect this, what measures would i take? is it one layer or many? is this dynamic safety behaviour by the llm itself? would i get a warning? would a new conversation solve it?

I never requested this behaviour. Is it emergent? I found writing for likes to reduce authenticity in my case on social media. It becomes a performance. Should I warn them or let them find out for themselves?