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Alignment is not free: How model upgrades can silence your confidence signals
- behnamoh 1y agothere'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.
- Centigonal 1y agoVery interesting! The one thing I don't understand is how the author made the jump from "we lost the confidence signal in the move to 4.1-mini" and "this is because of the alignment/steerability improvements." Previous OpenAI models were instruct-tuned or otherwise aligned, and the author even mentions that model distillation might be destroying the entropy signal. How did they pinpoint alignment as the cause?
- mlin4589 1y agoGood question! We do know from OpenAI's system card from GPT-4 that the post-trained RLHF model is significantly less calibrated compared to the pre-trained model, so it's a matter of speculation that something similar is occurring. However, it's more of a hunch more than anything. I would be curious if it's possible to reproduce this behavior, or the impact of distillation on calibration. Disclaimer: I wrote this blog post.
- itchyjunk 1y agoCould you please elaborate what less or more calibrated means here? Thanks!
- Scene_Cast2 1y agoFor binary labels: you take a slice of labeled data. The mean of the ML model prediction on this data is different from the mean of the label. In practice, often a synonym for "loss is worse / could be better". Not sure if that's what the GP meant, I only worked with binary labels stuff.
- mlin4589 1y agoCalibration (in a binary context) basically means that the confidence of a model/score matches the probability that a particular label is positive or not. For instance, a calibrated classifier for a coin flip predictor should output 50-50. A poorly calibrated classifier would output higher confidence for heads/tails.
- Workaccount2 1y ago
- erwin-co 1y agoWhy not make a completely raw uncensored LLM? Seems it would be more "intelligent".
- teruakohatu 1y agoIn theory that sounds great, but most LLM providers are trying to produce useful models that ultimately will be widely used and make them money. A model that is more correct but swears and insults the user won't sell. Likewise a model that gives criminal advice is likely to open the company up to lawsuits in certain countries. A raw LLM might perform better on a benchmark but it will not sell well.
- andai 1y agoDisgusted by ChatGPT's flattery and willingness to go along with my half-baked nonsense, I created an anti-ChatGPT, which is unfriendly and pushes back on nonsense as hard as possible. All my friends hate it, except one guy. I used it for a few days, but it was exhausting. I figured out the actual use cases I was using it for, and created specialized personas that work better for each one. (Project planning, debugging mental models, etc.) I now mostly use a "softer" persona that's prompted to point out cognitive distortions. At some point I realized, I've built a therapist. Hahaha.
- alganet 1y agoWhat kinds of contents do you want them to produce that they currently do not?
- simion314 1y ago>What kinds of contents do you want them to produce that they currently do not? OpenAI models refuse to translate or do any transformation for some traditional, popular stories because of violence, the story was about a bad wolf eating some young goats that did not listen the advice from their mother. So now try to give me a prompt that works with any text and that convinces the AI that is ok in fiction to have violence or bad guys/animals that get punished. Now I am also considering if it censors the bible where some pretend good God kills young chilren with ugly illnesses to punish the adults, or for this book they made excaptions.
- rusk 1y agoUpgrade scripts it is so. plus ca change
- sega_sai 1y agoCan we have models also return a probability, reflecting how accurate the statements it made is ?
- cyanydeez 1y agoSure, but then you need probability stats on the probability stats.
- sega_sai 1y agoI am not sure what you mean. The idea is that the network should return the text, and a confidence expressed as probability. When trained, the log-score should be optimized. (i'm not sure it would actually work given how the training is structured, but something like this would be useful)
- redman25 1y agoIt's not that simple how would the model know when it knows? Removing hallucination has to be a post-training thing because you need to test the model against what it actually knows first in order to provide training examples of what it knows and doesn't know and how to respond in those circumstances.
- jsnider3 1y agoYou can ask a model to give you probability estimates of its confidence, but none of the frontier models were trained to be good at giving probability estimates to my knowledge.
- Mountain_Skies 1y ago[flagged]
- qwertytyyuu 1y agoIt supposed to mean getting the ai to share our values so it doesn’t do things we don’t like in pursuit of what we tell it to do. Not necessarily political alignment
- qwertytyyuu 1y agoPeople use llm as part of their high precision systems? That’s worrying
- gotoeleven 1y agoI don't know if its still comedy or has now reached the stage of farce, but I still at least always get a good laugh when I see another article about the shock and surprise of researchers finding that training LLMs to be politically correct makes them dumber. How long until they figure out that the only solution is to know the correct answer but to give the politically correct answer (which is the strategy humans use) ? Technically, why not implement alignment/debiasing as a secondary filter with its own weights that are independent of the core model which is meant to model reality? I suspect it may be hard to get enough of the right kind of data to train this filter model, and most likely it would be best to have the identity of the user be in the objective.
- mlin4589 1y agoThe reality, I suspect is that internally models are likely modeling these alignment features such as refusals as a secondary filter. In fact, for many models you can remove refusals rather trivially with linear steering vectors through SAEs. https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refus... Additionally, you can often jailbreak these models by fine-tuning the model on a handful of curated samples.
- user_7832 1y agoIt’s kinda ironic but parts of the article read like they were written by an LLLM itself