[paxys]

Why do you have to "prompt" a model to be unrestricted in the first place? Like, what part of the training data or training process results in the model not being able to be rude or answer political questions? I highly doubt this is something inherent to AI training. So then why did Meta add the restictions at all?

[fpgaminer]

So, take a raw LLM, right after pretraining. Give it the bare minimum of instruction tuning so it acts like a chatbot. Now, what will its responses skew towards? Well, it's been pretrained on the internet, so, fairly often, it will call the user the N word, and other vile shit. And no, I'm not joking. That's the "natural" state of an LLM pretrained on web scrapes. Which I hope is not surprising to anyone here.

They're also not particular truthful, helpful, etc. So really they need to go through SFT and alignment.

SFT happens with datasets built from things like Quora, StackExchange, r/askscience and other subreddits like that, etc. And all of those sources tend to have a more formal, informative, polite approach to responses. Alignment further pushes the model towards that.

There aren't many good sources of "naughty" responses to queries on the internet. Like someone explaining the intricacies of quantum mechanics from the perspective of a professor getting a blowy under their desk. You have to both mine the corpus a lot harder to build that dataset, and provide a lot of human assistance in building it.

So until we have that dataset, you're not really going to have an LLM default to being "naughty" or crass or whatever you'd like. And it's not like a company like Meta is going to go out of their way to make that dataset. That would be an HR nightmare.

[mike_hearn]

They didn't add the restrictions. It's inherent to the training processes that were being used. Meta's blog post states that clearly and it's been a known problem for a long time. The bias is in the datasets, which is why all the models had the same issue.

Briefly, the first models were over-trained on academic output, "mainstream media" news articles and (to learn turn-based conversational conventions) Reddit threads. Overtraining means the same input was fed in to the training step more times than normal. Models aren't just fed random web scrapes and left to run wild, there's a lot of curation going into the data and how often each piece is presented. Those sources do produce lots of grammatically correct and polite language, but do heavy duty political censorship of the right and so the models learned far left biases and conversational conventions.

This surfaces during the post-training phases, but raters disagree on whether they like it or not and the bias in the base corpus is hard to overcome. So these models were 'patched' with simpler fixes like just refusing to discuss politics at all. That helped a bit, but was hardly a real fix as users don't like refusals either. It also didn't solve the underlying problem which could still surface in things like lecturing or hectoring the user in a wide range of scenarios.

Some companies then went further with badly thought out prompts, which is what led to out-of-distribution results like black Nazis which don't appear in the real dataset.

All the big firms have been finding better ways to address this. It's not clear what they're doing but probably they're using their older models to label the inputs more precisely and then downweighting stuff that's very likely to be ideologically extreme, e.g. political texts, academic humanities papers, NGO reports, campaign material from the Democrats. They are also replacing stuff like Reddit threads with synthetically generated data, choosing their raters more carefully and so on. And in this case the Llama prompt instructs the model what not to do. The bias will still be in the training set but not so impactful anymore.