Interesting how that once again matches human intuition: It's easier to know what you don't know, than to know what is true.
haha, interesting analogy
Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong
41–50 of 55 posts
Re: Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong
#42[flagged]
Re: Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong
#43> "post-trained to know when it's wrong" Is it also post-trained to know when it's wrong about when it's wrong? > "Every response comes with a confidence score between 0 and 1" How confident is it in its confidence? Please, I'm sure that what you're doing is very neat and useful, but use other language to describe it. I beg you. You can't know when you're wrong. You can only know when you're unsure or inconsistent. Y…
A few years ago, before RLVR became ubiquitous, people were using generate -> classify loops to get models to improve math reasoning. Turns out classification is sometimes easier to do and more accurate than generating coherent traces. So you'd get a model to "think step by step", then run each step (.split('/n/n')) through a classifier, and tell the model "this step might be wrong, re-check". If you're interested I…
I always thought it was a pretty noisy signal, was there any empirical work showing what the SNR was in these step by step classification loops?
Re: Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong
#44> "post-trained to know when it's wrong" Is it also post-trained to know when it's wrong about when it's wrong? > "Every response comes with a confidence score between 0 and 1" How confident is it in its confidence? Please, I'm sure that what you're doing is very neat and useful, but use other language to describe it. I beg you. You can't know when you're wrong. You can only know when you're unsure or inconsistent. Y…
A few years ago, before RLVR became ubiquitous, people were using generate -> classify loops to get models to improve math reasoning. Turns out classification is sometimes easier to do and more accurate than generating coherent traces. So you'd get a model to "think step by step", then run each step (.split('/n/n')) through a classifier, and tell the model "this step might be wrong, re-check". If you're interested I…
None of what you've said has anything to do with correctness, only certainty. And it's funny to me in context if you're certain that the opposite is true.
> "It's also worth mentioning that LMs are wrong in different ways from humans."
This to me only shows a poor and incomplete knowledge of humans and of wrongness and its causes. If you look at all of the humans and all of the times, you find exhibitions of all the same ways of being wrong. At best you can say that some (<all) LMs are wrong in different ways than some (<all) humans at some (<all) times, but that's as good as saying that different humans are wrong in different ways than different other humans at different times.
Re: Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong
#45[flagged]
Re: Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong
#46> "post-trained to know when it's wrong" Is it also post-trained to know when it's wrong about when it's wrong? > "Every response comes with a confidence score between 0 and 1" How confident is it in its confidence? Please, I'm sure that what you're doing is very neat and useful, but use other language to describe it. I beg you. You can't know when you're wrong. You can only know when you're unsure or inconsistent. Y…
This is an old problem in philosophy, one that the pre-LLM systems were interested in, which got completely bypassed by the present approach. What is knowledge? It's not a simple question even for humans, we don't have a magic truth detector in the brain. https://plato.stanford.edu/entries/knowledge-analysis/ This is why LLMs are more successful in programming than other disciplines: it's easier to assess correctness…
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Re: Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong
#47Earlier quoted context omitted.
It seems to me trivial to verify: just ask.
Opinions can only be seen as facts when modelled as time series data. Verifying an opinion X at time T does not entail X at time T+1.
_All_ statements are only valid inside the window within which their understandable context stays constant, facts or otherwise.
> "Verifying an opinion X at time T does not entail X at time T+1."
And verifying that my cat weighs 9.5 lbs now does not entail that he will weigh 9.5 lbs tomorrow, but "my cat weighs 9.5 lbs" is not an opinion; it's a statement of fact from measurement/observation and implies "extrapolated from the last specific check done in the usual way" unless explicitly stated otherwise because that's how language works in polite society. "I like green" is the same.
"My cat weighs 9.5 lbs" is only true within a context of a particular moment and location and measurement apparatus and understanding of cat and ownership and 9.5 and lbs.
"1+1=2" is only true within a context of what "1" and "2" and "+" and "=" mean.
And so on.
Re: Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong
#48Re: Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong
#49Love what you're doing at cactus, keep it up!!
Re: Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong
#50Earlier quoted context omitted.
Opinions can only be seen as facts when modelled as time series data. Verifying an opinion X at time T does not entail X at time T+1.
_All_ statements are only valid inside the window within which their understandable context stays constant, facts or otherwise. > "Verifying an opinion X at time T does not entail X at time T+1." And verifying that my cat weighs 9.5 lbs now does not entail that he will weigh 9.5 lbs tomorrow, but "my cat weighs 9.5 lbs" is not an opinion; it's a statement of fact from measurement/observation and implies "extrapolated…
This seems like a very long winded way to agree with my statement that opinions can only be considered facts when modelled as time series data.