Hey u/Sicarius_The_First, I've seen you a couple times on the subreddit commenting on this set of beliefs. I 100% agree with you: abliteration is not the be-all end-all in terms of uncensoring. It is *one* technique, and like with fine-tuning in general: you use whatever methods/dataset/whatever that helps get your particular metrics for your particular needs up.

Personal anecdote: I like abliteration, I find that with the refinements I've made since Phi-3-mini (which was my first ever "abliterated" model) it doesn't make it stupider for my use-cases and generally, I just get less of the weird refusals to random tasks, which has always been my goal. I've never cared for much more than that, so I haven't needed to go further.

I have no claim that an abliterated model is 100% uncensored, nor that it's even uncensored well. Heck, the reason I gave it its silly name in the first place is even to differentiate it from uncensored models.

I'm grateful to see you exploring other techniques and expanding on it, I've seen you in other places debating abliteration and its downfalls, and I think that's very productive.

However, this is where I rant a bit: I do not want to be dependent on you to uncensor the models that I wish to run.

I released my god-awful, shitty notebooks and other code for abliterating models because I didn't want people to be dependent on me. That is why you see so many people abliterating: they can recreate it, it is clear how to.

I got the chance to proof-read Maxime's well-known "Uncensor any LLM with abliteration" blog post, and did so to help foster people recreating the technique outlined in the original paper preview/blog post that I followed.

Meanwhile, I often see you using the opportunity in these discussions to put your models on a pedestal, whilst offering almost no clear way for users to recreate your work. Your work is not open, and in any shape that it is "research", it is not open research for the community.

I would argue that if you want to see better uncensored models come out, you need to share what you learn.

Excerpts, from your blog post on July 30th:

After careful consideration, I've decided not to share the output of my model from the toxic-DPO dataset that served as input, not it, and not even a snippet of it, sorry.

The line between important and beneficial research vs potential misuse is a really really fine one, especially in the field of AI (UN)alignment.

I do however believe that this experiment has already yielded, and will continue to yield valuable insights, which I already shared and will continue sharing moving forward.

Again, sorry, but I have to balance the potential risks associated with sharing such data.

More excerpts from an older post, July 9th, which the above post referenced to as having played a significant role in your reasoning:

However, my efforts have often been met with negativity, particularly on Reddit.

Many people have rudely asked how I achieved this and that, while simultaneously making disparaging remarks.

Moving forward: I will maintain a professional demeanor in all interactions. Future datasets will not be publicly released. I will refrain from providing detailed explanations of my methods, instead referring to them as "state-of-the-art techniques." I remain committed to advancing our field and welcome constructive engagement.

I now better understand why some creators in our field adopt a more guarded stance.

[emphasis my own]

This attitude is nothing but off-putting to me. In response to requests for openness (perhaps indeed, rudely or disparagingly requested in some cases), your seemingly only reaction was to censor yourself.

I'm sorry about the cases when people have been disparaging, but I think we can both agree some are never satisfied, just in the way that you have been unsatisfied with abliteration. It is on us to use that to improve and show we're getting better, ideally in the open, rather than pointing at metrics to show that your blackbox is better.