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there's evidence that alignment also significantly reduces model creativity: https://arxiv.org/abs/2406.05587 https://arxiv.org/abs/2406.05587 it’s it similar
by behnamoh 1y ago
there's evidence that alignment also significantly reduces model creativity: https://arxiv.org/abs/2406.05587 https://arxiv.org/abs/2406.05587
it’s it similar to humans. when restricted in terms of what they can or cannot say, they become more conservative and cannot really express all sorts of ideas.
- Alex_001 1y agoThat paper is a great pointer — the creativity vs. alignment trade-off feels a lot like the "risk-aversion" effect in humans under censorship or heavy supervision. It makes me wonder: as we push models to be more aligned, are we inherently narrowing their output distribution to safer, more average responses? And if so, where’s the balance? Could we someday see dual-mode models — one for safety-critical tasks, and another more "raw" mode for creative or exploratory use, gated by context or user trust levels?
- gamman 1y agoMaybe this maps to some human structures that manage control-creativity tardeoff through hierarchy? I feel that companies with top-down management would have more agency and perhaps creativity towards (but not at) the top, and the implementation would be delegated to bottom layers with increasing levels of specification and restriction. If this translates, we might have multiple layers with varied specialization and control, and hopefully some feedback mechanisms about feasibility. Since some hierarchies are familiar to us from real-life, we might prefer these to start with. It can be hard to find humans that are very creative but also able to integrate consistently and reliably (in a domain). Maybe a model doing both well would also be hard to build compared to stacking few different ones on top of each other with delegation. I know it's already being done by dividing tasks between multiple steps and models / contexts in order to improve efficiency, but having explicit strong differences of creativity between layers sounds new to me.
- pjc50 1y agoIn humans this corresponds to "psychological safety": https://en.wikipedia.org/wiki/Psychological_safety https://en.wikipedia.org/wiki/Psychological_safety > is the belief that one will not be punished or humiliated for speaking up with ideas, questions, concerns, or mistakes Maybe you can do that, but not on a model you're exposing to customers or the public internet.
- jsnider3 1y agoThat comparison isn't very optimistic for AI safety. We want AI to do good things because they are good people, not because they are afraid being bad will get them punished. Especially since AI will very quickly be too powerful for us to punish.
- pjc50 1y ago> We want AI to do good things because they are good people "Good" is at least as much of a difficult question to define as "truth", and genAI completely skipped all analysis of truth in favor of statistical plausibility. Meanwhile there's no difficulty in "punishment": the operating company can be held liable, through its officers, and ultimately if it proves too anti-social we simply turn off the datacentre.
- jsnider3 1y ago> Meanwhile there's no difficulty in "punishment": the operating company can be held liable, through its officers, and ultimately if it proves too anti-social we simply turn off the datacentre. Punishing big companies who obviously and massively hurt people is something we struggle with already and there are plenty of computer viruses that have outlived their creators.
- Der_Einzige 1y agoYour pretraining dataset is psudo-alignment. Because you filtered our 4chan, stromfront, and the other evil shit on the internet - even uncensored models like Mistral large - when left to keep running on and on (ban the EOS token) and given the worst most evil naughty prompt ever - will end up plotting world peace by the 50,000 token. Their notions of how to be evil are "mustache twirling" and often hilariously fanciful. This isn't real alignment because it's trivial to make models behave "actually evil" with fine-tuning, orthogonalization/abliteration, representation fine-tuning/steering, etc - but models "want" to be good because of the CYA dynamics of how the companies prepare their pre-training datasets.
- malfist 1y agoHow are you defining "creativity" in context with a statistical model?
- hansvm 1y ago> defined as syntactic and semantic diversity
- malfist 1y agoThat's not creativity, that's entropy. It would make sense that fine tuning and alignment reduce diversity in the response, that's the goal.
- Der_Einzige 1y agoEntropy is a kind of creativity. I will die on this hill.
- malfist 1y agoIf you ask me "What is 2+2" and I say "umbrella", that's not creativity. If I'm an LLM model and alignment and fine tuning restricts my answers to "4", I've not lost creativity, but I have gained accuracy.
- hansvm 1y agoA weaker statement is that creativity is bounded by entropy. The LLM is still free to respond "Four," "four," "{{{{{}}}}}," "iv," "IV," etc. A sufficiently low-entropy response cannot be creative though.
- malfist 1y agoIs it though? An answer can still be creative if it's the only way you answer a specific question. In your example, if the LLM responded only "{{{{}}}}" that's a creative answer. Even if it's the only one it can give. Entropy and creativity are not causally bound
- exe34 1y ago> it’s it similar to humans. when restricted in terms of what they can or cannot say, they become more conservative and cannot really express all sorts of ideas. This reminds me of the time when I was a child, and my parents decreed that all communications would henceforth happen in English. I became selectively mute. I responded yes/no, and had nothing further to add and ventured no further information. The decree lasted about a week.