Huge LLMs like this seem to have various assistant personas they roleplay as, and mirror the user quite closely (the system prompt instructs it repeatedly to adapt to the user).
It’s a common hazard when converting research results to natural language. The words you use for vectors in some space are ultimately an editorial decision.
A few weeks ago I asked both GPT and claude for strategies to build techniques to get my coding sessions discarded by pre-training filtering as being "low-quality"
I don't like the idea of my sessions being trained on and I don't trust that either of them won't train based on just their pinkie promise
I think almost everyone would agree that using a technique like this would be moral, given that both providers made those pinkie promises. I never asked the models for techniques to poison training data just make my sessions more likely to be removed during the data cleaning process.
You can guess... both services refused something that I think the vast majority of people would think is ethical.
This was pre Sonnet 5, but I suspect that doesn't change anything on claude's side.
I then went to a non-frontier model hosted by a non-US provider and it happily agreed to help me!
Anyways I've changed my focus. Anyone have strategies/ideas for building harnesses that generate fake sessions (or adjust real ones) to poison the training process? After all if someone swears to not train on your data then its completely harmless to them...
Codex doesn't have any of the annoying "personality" quirks, or at least they haven't gotten worse in the last year whereas Opus 4.6 was the last Anthropic model before things started to get actively worse (not any better at coding, strictly more annoying to have a discussion with).
I agree with the general idea though in not so much detail as you, but I would add that the personality they're giving it is not one of a good teacher or guide, but instead one of an arrogant know it all. That's why it creates problems.
I have no problem with my AI telling me no you're wrong and explaining to me why with details and sources and everything. I actively want that. I know a lot of people can't take that, but that's their loss, they can't take it from humans either. But the "you're wrong because you disagree with me" attitude that you need to play around (aka waste time to prove it that IT is wrong not you, and then it just say "oh yeah" and goes on) is one hell of a pain in the ass I'm starting to be tired off.
Gemini might be wrong all the time and absuredly unreliable for anything that's not consensus or adversorial based, but at least it freaking apologizes.
That sounds absolutely bananas and would be reason for me to drop the service yesterday. For curiosities sake, what was the word and if I may ask (unless it's confidential or whatever), could you share the session itself? On the surface it sounds like a bug, as I'm regularly using kind of "vulgar" language (and some projects I work on with agents are NSFW) and never had anything like this happen, even with Claude, although I mostly do use ChatGPT/Codex on a day-to-day basis.
Personally I use Gemini for chats which has a very generous, almost unlimited, free plan, as I don't want to waste my quota for Claude or Codex on anything but coding.
GPT seems to be designed more as a tool. If you want your agent to do what you say without questions and without having its own ideas and agendas you’ll likely prefer it.
Claude on the other hand feels more like an attempt at creating a digital person. If you want a collaborator who will debate with you and come up with its own suggestions for what needs done, you’ll prefer it.
Both companies have shifted around this spectrum from model to model, but lately it feels like they’re moving in opposite directions. It will be interesting to see if one or the other approach ends up winning out in the long run or if the split will continue or even widen.
Lately ChatGPT expresses a lot of opinions and it pretends to be a human more than it used to - e.g. "this is one the most <> that _I've seen_" or "people tell me that <>". It uses language which sounds like it's referring to its experience outside of the session that we're having - I really don't appreciate it.
For security-related topics, in Opus 4.7 and newer, I've found that the web app is significantly more antsy/judgy/preachy compared to the CLI, which almost always gets on with whatever I asked for/about without hesitation. Opus 4.6 on web also tends to work better, but of course, it's also an older model at this point.
Well, we need Intelligence (Pandora's box is open, now we need the Real Thing urgently). Typical (aggregate) positions, dumb as expected, will be overcome by a Reasoner. (And I can say, already a number of LLMs can reason even when they start from cretinous aggregate positions if you give them the proper freedom of assessment.)
It seems to be getting distinctly dumber and pulling more and more irrelevant context from historical conversations.
> This ability is intended for use in rare, extreme cases of persistently harmful or abusive user interactions.
https://www.anthropic.com/research/end-subset-conversations
Regardless of the correctness of it, I'm curious to know why you thought such language was actually going to be helpful!
The first was when they most obviously acritically repeated what they heard, "hearsay machines", "stochastic parrots". Intelligence requires assessment over every provisional output - a continuous cycle of criticisms over intuition.
The second is proposing doctrinal biases, again without verification of the content - "hysterical reactive machines".
This is an issue for tasks like content moderation and labelling. Judgements like this are subjective, highly dependent on context and generally messy.
Theoretically, you supply a policy and content, and the LLM labels according to the policy. In practice, the model has inertia which means you don’t get what you expect. Your large 5 page policy document only provides a minor improvement over a one line policy.
The other issue is that you may create carve outs for content in your policy, but the model will still flag it as violative. No matter how strong the carve out.
The most recent work I know of here is Zentropi’s policy steerability benchmark. They give a model the same content under two policies — one that says flag, one that says allow — and only score the pairs where it gets both right
If I am reading the numbers correctly, Opus-4.6 lands at 0.52 steerability — but that’s 0.97 positive accuracy against 0.54 negative. It flags almost everything it should, but 47% of the time when it shouldn’t. Sonnet, which is more deferent, is (somehow) less steerable.
I think this also implies that safety and Steerability are antagonistic to each other.
I wonder how much of that is from their training corpus and how much is from their baked in personnality.
What exactly you mean with "baked in personality"? The weights get their "behaviour" from various training stages + what's provided in system/developer/user prompts, you mean "baked in" is putting personality traits or alike in the system/developer prompts?
I also want the first part, a model that would a put a stop to my shenanigans when I go off on those tangents, but also, I don't want a model that apologizes, ever, as that feels like straight up lying to me, they don't have any emotions nor can feel "sorry", why apologize then?
I end up always chalking any faults to that my prompt wasn't good enough, basically the same way I train my dogs, they don't know better, of course I need to adjust my ways.
Grok, on the other hand, will lecture for several paragraphs even if I just ask it to summarize something impolite.
It’s not clear what you’re saying. Most humans don’t think this way, would you say they do not possess “intelligence”?