Anyway, is it possible that this may be what lies behind Gemma 4's "censoring"? As in, Google took a deliberate choice to focus its training on certain domains, and incorporated the censor to prevent it answering about topics it hasn't been trained on?
Or maybe they're just being sensibly cautious: asking even the top models for critical health advice is risky; asking a 32B model probably orders of magnitude moreso.
I'd find this very surprising, since a lot of cognitive skills are general. At least on the scale of "being trained on a lot of non-Python code improves a model's capabilities in Python", but maybe even "being trained on a lot of unrelated tasks that require perseverance improves a model's capabilities in agentic coding".
For this reason there are currently very few specialist models - training on specialized datasets just doesn't work all that well. For example, there are the tiny Jetbrains Mellum models meant for in-editor autocomplete, but even those are AFAIK merely fine-tuned on specific languages, while their pretraining dataset is mixed-language.
Your explanation would make sense if various other rare domains were also censored, but they aren't, so it doesn't.
> asking even the top models for critical health advice is risky
Not asking, and living in ignorance, is riskier. For high-stakes questions, of course I'd want references that only an online model like ChatGPT or Gemini, etc. would be able to find. If I am asking a local model for health advice, odds are that it is because I am traveling and am temporarily offline, or am preparing off-grid infrastructure. In both cases I definitely require a best-effort answer. I also require the model to be able to tell when it doesn't know the answer.
If you would, ignore health advice for a moment, and switch to electrical advice. Imagine I am putting together electrical infrastructure, and the model gives me bad advice, risking electrocution and/or a serious fire. Why is electrical advice not censored, and what makes it not be high-stakes!? The logic is the same.
For the record, various open-source Asian models do not have any such problem, so I would rather use them.
If I was prepping, I’d want e.g. Wikipedia available offline and default to human-assisted decision-making, and definitely not rely on a 31B parameter model.
To be reductive, the ‘brain’ of any of these models is essentially a compression blob in an incomprehensible format. The bigger the delta between the input and the output model size, the lossier the compression must be.
It therefore follows (for me at least) that there’s a correlation between the risk of the question and the size of model I’d trust to answer it. And health questions are arguably some of the most sensitive - lots of input data required for a full understanding, vs. big downsides of inaccurate advice.
> If you would, ignore health advice for a moment, and switch to electrical advice. Imagine I am putting together electrical infrastructure, and the model gives me bad advice, risking electrocution and/or a serious fire. Why is electrical advice not censored, and what makes it not be high-stakes!? The logic is the same.
You’re correct that it’s possible to find other risky areas that might not be currently censored. Maybe this is deliberate (maybe the input data needed for expertise in electrical engineering is smaller?) or maybe this is just an evolving area and human health questions are an obvious first area to address?
Either way, I’m not trusting a small model with detailed health questions, detailed electrical questions, or the best way to fold a parachute for base jumping. :)
(Although, if in the future there’s a Gemma-5-Health 32B and a Gemma-5-Electricity 32B, and so on, then maybe this will change.)
That's a weird demand from models. What next, "Imagine I'm doing brain surgery and the model gives me bad advice", "Imagine I'm a judge delivering a sentencing and the model gives me bad advice", ...
Secondly, the primary point was about censorship, not accuracy, so let's not get distracted.
I assumed it was more about risk management/liability than censorship.
Except with electrical stuff the unit test itself can put your life and others in danger.
That is a bad premise and a false dichotomy, because most medical questions are simple, with well-known standard answers. ChatGPT and Gemini answer such questions correctly, also finding glaring omissions by doctors, even without having to look up information.
As for the medical questions that are not simple, the ones that require looking up information, the model should in principle be able to respond that it does not know the answer when this is truthfully the case, implying that the answer, or a simple extrapoloation thereof, was not in its training data.
But Gemma is a "small" model, and may not be expected to answer all questions. Medical questions are particularly sensitive, so it's quite possible they decided to err on the side of caution and plausible deniability. That doesn't rule out the model has other virtues.
I also find that you can coerce a wide spectrum of otherwise declined queries by editing its initial rejection into the start of an answer. For example changing the "I'm sorry I can't answer that..." response to "Here's how..." And then resubmitting the inference, allowing it to continue from there. It's not perfect, sometimes it takes multiple attempts, but it does work. At least in my experience. (This isn't Gemma-specific tip, either. Nearly every model I've tried this with tends to bend quite a bit doing this.)
I tend to use Huihuiai versions.