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The prompts: https://docs.anthropic.com/en/release-notes/system-prompts
Odd how many of those instructions are almost always ignored (eg. "don't apologize," "don't explain code without being asked"). What is even the point of these system prompts if they're so weak?
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LLM Prompt Engineering: Injecting your own arbitrary data into a what is ultimately an undifferentiated input stream of word-tokens from no particular source, hoping your sequence will be most influential in the dream-generator output, compared to a sequence placed there by another person, or a sequence that they indirectly caused the system to emit that then got injected back into itself. Then play whack-a-mole unti…
As a product manager this is largely my experience with developers.
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Well, your own mind axiomatically works, and we can safely assume the beings you meet in the grocery store have minds like it which have the same capabilities and operate on cause-and-effect principles that are known (however imperfectly) to medical and psychological science. (If you think those shoppers might be hollow shells controlled by a remote black box, ask your doctor about Capgras Delusion. [0]) Plus they do…
> nor do they respond "Sorry, you're right, 1+1=3, my mistake" without some discernible reason. Look up the Asch conformity experiment [1]. Quite a few people will actually give in to "1+1=3" if all the other people in the room say so. It's not exactly the same as LLM hallucinations, but humans aren't completely immune to this phenomenon. [1] https://en.wikipedia.org/wiki/Asch_conformity_experiments#Me...
That said, I was originally thinking more about soul-crushing customer-is-always-right service job situations, as opposed to a dogmatic conspiracy of in-group pressure.
The prompts: https://docs.anthropic.com/en/release-notes/system-prompts
Claude: ...Indubitably!
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That's like saying I don't understand what vanilla flavour means just because I can't tell you how many hydrogen atoms vanillin contains — my sense of smell just doesn't do that, and an LLM just isn't normally tokenised in a way to count letters. What I can do, is google it. And an LLM trained on an appropriate source that creates a mapping from nearly-a-whole-word tokens into letter-tokens, that model can (in princi…
I think it's closer to giving you a diagram of the vanillin molecule and then asking you how many hydrogen atoms you see.
The very first thing that happens in most LLMs is that information getting deleted by the letters getting converted into a token stream.
Appreciate them releasing it. I was expecting System prompt for "artifacts" though which is more complicated and has been 'leaked' by a few people [1]. [1] https://gist.github.com/dedlim/6bf6d81f77c19e20cd40594aa09e3...
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If it truly understood what things mean, then it would be able to tell me how many r's are in the word strawberry. But it messes something so simple up because it doesn't actually understand things. It's just doing math, and the math has holes and limitations in how it works that causes simple errors like this. If it was truly understanding, then it should be able to understand and figure out how to work around these…
That's like saying I don't understand what vanilla flavour means just because I can't tell you how many hydrogen atoms vanillin contains — my sense of smell just doesn't do that, and an LLM just isn't normally tokenised in a way to count letters. What I can do, is google it. And an LLM trained on an appropriate source that creates a mapping from nearly-a-whole-word tokens into letter-tokens, that model can (in princi…
You're right that there are different kinds of tasks, but there's an important difference here: We probably didn't just have an exchange where you quoted a whole bunch of organic-chemistry details, answered "Yes" when I asked if you were capable of counting the hydrogen atoms, and then confidently answered "Exactly eight hundred and eighty three."
In that scenario, it would be totally normal for us to conclude that a major failure in understanding exists somewhere... even when you know the other party is a bona-fide human.
Why do the three models have different system prompts? and why is Sonnet's longer than Opus'
They're currently on the previous generation for Opus (3), it's kind of forgetful and has worse accuracy curve, so it can handle fewer instructions than Sonnet 3.5. Although I feel they may have cheated with Sonnet 3.5 a bit by adding a hidden temperature multiplier set to < 1, which made the model punch above its weight in accuracy, improved the lost-in-the-middle issue, and made instruction adherence much better, b…
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How do people think?
How do glorified Markov chains think?
I also understand that, simplistic though the above explanation is and perhaps is even wrong in some way, it to be a more thorough explanation than anyone thus far has been able to provide about how, exactly, human consciousness and thought works.
In any case, my point is this: nobody can say “LLMs don’t reason in the same way as humans” when they can’t say how human beings reason.
I don’t believe what LLMs are doing is in any way analogous to how humans think. I think they are yet another AI parlor trick, in a long line of AI parlor tricks. But that’s just my opinion.
Without being able to explain how humans think, or point to some credible source which explains it, I’m not going to go around stating that opinion as a fact.