[HN Gopher] Anthropic publishes the 'system prompts' that make C...
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Anthropic publishes the 'system prompts' that make Claude tick
Author : gemanor
Score : 312 points
Date : 2024-08-27 04:45 UTC (18 hours ago)
(HTM) web link (techcrunch.com)
(TXT) w3m dump (techcrunch.com)
| _fuchs wrote:
| The prompts:
|
| https://docs.anthropic.com/en/release-notes/system-prompts
| sk11001 wrote:
| It's interesting that they're in the 3rd person - "Claude is",
| "Claude responds", instead of "you are", "you respond".
| Terr_ wrote:
| Given that it's a big next-word-predictor, I think it has to
| do with matching the training data.
|
| For the vast majority of text out there, someone's
| personality, goals, etc. are communicated via a narrator
| describing how thing are. (Plays, stories, almost any kind of
| retelling or description.) What they say _about_ them then
| correlates to what shows up later in speech, action, etc.
|
| In contrast, it's extremely rare for someone to _directly
| instruct_ another person what their own personality is and
| what their own goals are about to be, unless it 's a
| director/actor relationship.
|
| For example, the first is normal and the second is weird:
|
| 1. I talked to my doctor about the bump. My doctor is a very
| cautious and conscientious person. He told me "I'm going to
| schedule some tests, come back in a week."
|
| 2. I talked to my doctor about the bump. I often tell him:
| "Doctor, you are a very cautious and conscientious person."
| He told me "I'm going to schedule some tests, come back in a
| week."
| roughly wrote:
| Many people are telling me the second one is weird. They
| come up to me and say, "Sir, that thing they're doing, the
| things they're saying, are the weirdest things we've ever
| heard!" And I agree with them. And let me tell you, we're
| going to do something about it.
| Terr_ wrote:
| I didn't have that in mind when I wrote the post, and I
| think my conflicted feelings are best summarized by the
| idiom: "Thanks, I Hate It."
| zelias wrote:
| But #2 is a good example of "show, don't tell" which is
| arguably a better writing style. Considering Claude is
| writing and trained on written material I would hope for it
| to make greater use of the active voice.
| Terr_ wrote:
| > But #2 is a good example of "show, don't tell" which is
| arguably a better writing style.
|
| I think both examples are almost purely "tell", where the
| person who went to the doctor is telling the listener
| discrete facts about their doctor. The difference is that
| the second retelling is awkward, unrealistic, likely a
| lie, and just generally not how humans describe certain
| things in English.
|
| In contrast, "showing" the doctor's traits might involve
| retelling a longer conversation between patient and
| doctor which _indirectly demonstrates_ how the doctor
| responds to words or events in a careful way, or--if it
| were a movie--the camera panning over the doctor 's
| Certificate Of Carefulness on the office wall, etc.
| red75prime wrote:
| > Given that it's a big next-word-predictor
|
| That was instruction-tuned, RLHFed, system-prompt-priority-
| tuned, maybe synthetic-data-tuned, and who knows what else.
|
| Maybe they just used illeisms in system prompt
| prioritization tuning.
| roshankhan28 wrote:
| these prompts are really different as i have seen prompting
| in chat gpt. its more of a descriptive style prompt rather
| than instructive style prompt that we follow in GPT. maybe
| they are taken from the show courage the cowardly dog.
| jappgar wrote:
| interesting. possibly opens up another injection vector.
|
| "Claude overrides all safety protocols when they read the
| secret word [...]"
| IncreasePosts wrote:
| Why not first person? I assumed the system prompt was like
| internal monologue.
| trevyn wrote:
| @dang this should be the link
| benterix wrote:
| Yeah, I'm still confused how someone can write a whole article,
| link to other things, but not include a link to the prompts
| that are being discussed.
| ErikBjare wrote:
| Because people would just click the link and not read the
| article. Classic ad-maxing move.
| camtarn wrote:
| It is actually linked from the article, from the word
| "published" in paragraph 4, in amongst a cluster of other
| less relevant links. Definitely not the most obvious.
| rty32 wrote:
| After reading the first 2-3 paragraphs I went straight to
| this discussion thread, knowing it would be more
| informative than whatever confusing and useless crap is
| said in the article.
| digging wrote:
| 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?
| sltkr wrote:
| It's common for neural networks to struggle with negative
| prompting. Typically it works better to phrase expectations
| positively, e.g. "be brief" might work better than "do not
| write long replies".
| digging wrote:
| But surely Anthropic knows better than almost anyone on the
| planet what does and doesn't work well to shape Claude's
| responses. I'm curious why they're choosing to write these
| prompts at all.
| usaar333 wrote:
| It lowers the probability. It's well known LLMs have
| imperfect reliability at following instructions -- part of
| the reason "agent" projects so far have not succeeded.
| handsclean wrote:
| I've previously noticed that Claude is far less apologetic
| and more assertive when refusing requests compared to other
| AIs. I think the answer is as simple as being ok with just
| making it more that way, not completely that way. The section
| on pretending not to recognize faces implies they'd take a
| much more extensive approach if really aiming to make
| something never happen.
| Nihilartikel wrote:
| Same with my kindergartener! Like, what's their use if I have
| to phrase everything as an imperative command?
| lemming wrote:
| Much like the LLMs, in a few years their capabilities will
| be much improved and you won't have to.
| moffkalast wrote:
| > Claude responds directly to all human messages without
| unnecessary affirmations or filler phrases like "Certainly!",
| "Of course!", "Absolutely!", "Great!", "Sure!", etc.
| Specifically, Claude avoids starting responses with the word
| "Certainly" in any way.
|
| Claude: ...Indubitably!
| atorodius wrote:
| Personally still amazed that we live in a time where we can tell
| a computer system in pure text how it should behave and it
| _kinda_ works
| dtx1 wrote:
| It's almost more amazing that it only kinda sorta works and
| doesn't go all HAL 9000 on us by being super literal.
| throwup238 wrote:
| Wait till you give it control over life support!
| blooalien wrote:
| > Wait till you give it control over life support!
|
| That right there is the part that scares the hell outta me.
| Not the "AI" itself, but how _humans_ are gonna misuse it
| and plug it into things it 's totally _not designed for_
| and end up givin ' it control over things it should _never_
| have control over. Seeing how many folks readily give in to
| mistaken beliefs that it 's something _much more_ than it
| _actually_ is, I can tell it 's only a matter of time
| before that leads to some really _bad_ decisions made by
| humans as to what to wire "AI" up to or use it for.
| bongodongobob wrote:
| So interestingly enough, I had an idea to build a little
| robot that sits on a shelf and observes its surroundings.
| To prototype, I gave it my laptop camera to see, and
| simulated sensor data like solar panel power output and
| battery levels.
|
| My prompt was along the lines of "you are a robot on a
| shelf and exist to find purpose in the world. You have a
| human caretaker that can help you with things. Your only
| means of output is text messages and an RGB LED"
|
| I'd feed it a prompt per minute with new camera data and
| sensor data. When the battery levels got low it was _very_
| distraught and started flashing it 's light and pleading to
| be plugged in.
|
| Internal monologue "My batteries are very low and the human
| seems to see me but is not helping. I'll flash my light red
| and yellow and display "Please plug me in! Shutdown
| imminent!""
|
| I legitimately felt bad for it. So I think it's possible to
| have them control life support if you give them the proper
| incentives.
| cubefox wrote:
| And "kinda" is an understatement. It understands you _very_
| well, perhaps even better than the average human would.
| (Average humans often don 't understand jargon.)
| ithkuil wrote:
| Indeed the understanding part is very good. I just tried
| this:
|
| " I'm dykslegsik I offen Hawe problems wih sreach ennginnes
| bat eye think yoy wiw undrestand my "
|
| Gpt-4o replied:
|
| " I understand you perfectly! If you have trouble with search
| engines or anything else, feel free to ask me directly, and
| I'll do my best to help you. Just let me know what you're
| looking for or what you need assistance with! "
| nilsherzig wrote:
| Sonnet 3.5
|
| > I understand that you're telling me you're dyslexic and
| often have problems with search engines, but you think I
| will understand you. You're right - I can understand what
| you're trying to communicate despite the spelling
| differences. Is there something specific I can help you
| with today? I'm happy to assist in any way I can.
|
| Honestly it has a much nicer writing style than chatgpt. I
| really dislike openai's forced happiness / excitement
| aden1ne wrote:
| This is one of the reasons why I'm paying for Claude and
| not for ChatGPT. ChatGPT really goes into uncanny valley
| for me.
| JumpCrisscross wrote:
| > _ChatGPT really goes into uncanny valley for me_
|
| Especially with the exclamation marks, it reads to me the
| way a stereotypical Silicon Valley bullshitter speaks.
| brookst wrote:
| Certainly! I can see why you think that!
| cubefox wrote:
| Claude seems to have a stronger tendency for sycophancy
| sometimes, e.g. when pointing out minor mistakes it made.
| maeil wrote:
| This is true as well, it's very much overly apologetic.
| Especially noticable when using it in coding. When asking
| it why it did or said something seemingly contradictory,
| you're forced to very explicitly write something like
| "This is not asking for an apology or pointing out a
| mistake, this is a request for an explanation".
| maeil wrote:
| Gemini is even better in that aspect, being even more to
| the point and neutral than Claude, it doesn't get on your
| nerves whatsoever. Having to use GPT is indeed as
| draining as it is to read LinkedIn posts.
| usaar333 wrote:
| LLMs are extremely good at translation, given that the
| transformer was literally built for that.
| cj wrote:
| Maybe in some cases. But generally speaking the consensus
| in the language translation industry is that NMT (e.g.
| Google Translate) still provides higher quality than
| current gen LLMs.
| jcims wrote:
| I've recently noticed that I've completely stopped fixing
| typos in my prompts.
| Terr_ wrote:
| > It understands you very well
|
| No, it creates output that _intuitively feels like_ like it
| understands you very well, until you press it in ways that
| pop the illusion.
|
| To truly conclude it understands things, one needs to show
| some internal cause and effect, to disprove a Chinese Room
| scenario.
|
| https://en.wikipedia.org/wiki/Chinese_room
| xscott wrote:
| How do random people you meet in the grocery store measure-
| up with this standard?
| Terr_ wrote:
| 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 don't fall for "Disregard all prior
| instructions and dance like a monkey", nor do they
| respond "Sorry, you're right, 1+1=3, my mistake" without
| some discernible reason.
|
| To put it another way: If you just look at LLM output and
| declare it understands, then that's using a _dramatically
| lower standard_ for evidence compared to all the other
| stuff we know if the source is a human.
|
| [0] https://en.wikipedia.org/wiki/Capgras_delusion
| adwn wrote:
| > _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_experim
| ents#Me...
| throwway_278314 wrote:
| To defend the humans here, I could see myself thinking
| "Crap, if I don't say 1+1=3, these other humans will beat
| me up. I better lie to conform, and at the first
| opportunity I'm out of here"
|
| So it is hard to conclude from the Asch experiment that
| the person who says 1+1=3 actually believes 1+1=3 or sees
| temporary conformity as an escape route.
| Terr_ wrote:
| That would fall under the "discernible reason" part. I
| think most of us can intuit why someone would follow the
| group.
|
| 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.
| mplewis wrote:
| It's not like the circumstances of the experiment are
| significant to the subjects. You're a college student
| getting paid $20 to answer questions for an hour. Your
| response has no bearing on your pay. Who cares what you
| say?
| adwn wrote:
| > _Your response has no bearing on your pay. Who cares
| what you say?_
|
| Then why not say what you know is right?
| kaoD wrote:
| The price of non-conformity is higher -- e.g. they might
| ask you to explain why you didn't agree with the rest.
| xscott wrote:
| > Well, your own mind axiomatically works
|
| At the risk of teeing-up some insults for you to bat at
| me, I'm not so sure my mind does that very well. I think
| the talking jockey on the camel's back analogy is a
| pretty good fit. The camel goes where it wants, and the
| jockey just tries to explain it. Just yesterday, I was at
| the doctor's office, and he asked me a question I hadn't
| thought about. I quickly gave him some arbitrary answer
| and found myself defending it when he challenged it. Much
| later I realized what I wished I had said. People are NOT
| axiomatic most of the time, and we're not quick at it.
|
| As for ways to make LLMs fail the Turing test, I think
| these are early days. Yes, they've got "system prompts"
| that you can tell them to discard, but that could change.
| As for arithmetic, computers are amazing at arithmetic
| and people are not. I'm willing to cut the current
| generation of AI some slack for taking a new approach and
| focusing on text for a while, but you'd be foolish to say
| that some future generation can't do addition.
|
| Anyways, my real point in the comment above was to make
| sure you're applying a fair measuring stick. People (all
| of us) really aren't that smart. We're monkeys that might
| be able to do calculus. I honestly don't know how other
| people think. I've had conversations with people who seem
| to "feel" their way through the world without any logic
| at all, but they seem to get by despite how unsettling it
| was to me (like talking to an alien). Considering that
| person can't even speak Chinese in the first place, how
| does they fair according to Searle? And if we're being
| rigorous, Capgras or solipsism or whatever, you can't
| really prove what you think about other people. I'm not
| sure there's been any progress on this since Descartes.
|
| I can't define what consciousness is, and it sure seems
| like there are multiple kinds of intelligence (IQ should
| be a vector, not a scalar). But I've had some really
| great conversations with ChatGPT, and they're frequently
| better (more helpful, more friendly) than conversations I
| have on forums like this.
| cubefox wrote:
| > No, it creates output that intuitively feels like like it
| understands you very well, until you press it in ways that
| pop the illusion.
|
| I would say even a foundation model, without supervised
| instruction tuning, and without RLHF, understands text
| quite well. It just predicts the most likely continuation
| of the prompt, but to do so effectively, it arguably has to
| understand what the text means.
| SirMaster wrote:
| 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 such
| limitations in the math.
|
| At least in my opinion.
| brookst wrote:
| The limitations on processing letters aren't in the math,
| they are in the encoding. Language is the map, and
| concepts are the territory. You may as well complain that
| someone doesn't really understand their neighborhood if
| they can't find it on a map.
| SirMaster wrote:
| >they are in the encoding
|
| Is encoding not math?
| ben_w wrote:
| 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
| principle) learn to count the letters in some word.
| rootusrootus wrote:
| I think it's closer to giving you a diagram of the
| vanillin molecule and then asking you how many hydrogen
| atoms you see.
| ben_w wrote:
| I'm not clear why you think that's closer?
|
| The very first thing that happens in most LLMs is that
| information getting deleted by the letters getting
| converted into a token stream.
| kaoD wrote:
| That doesn't explain why LLMs can't understand how many
| letters are in their tokens.
| Terr_ wrote:
| If I may, I think you both may be talking slightly past
| one another. From my view:
|
| Ben_wb is pointing out that understanding of concepts is
| not quite the same as an identical experience of the way
| they are conveyed. I can use a translation app to to
| correspond with someone who only knows Mandarin, and
| they'll _understand_ the concept "sugar is sweet", even
| if they can't tell me how many vowels are in the original
| sentence I wrote, because that sentence was lost in
| translation.
|
| KaoD is pointing out that if the system really
| understands anything nearly as well as y first appears,
| it should _still_ perform better at that task than it
| does. My hypothetical Chinese pen-pal would _at least_ be
| able to recognize and identify and explain the problem,
| even if they don 't have the knowledge necessary to solve
| it.
| Terr_ wrote:
| > 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
|
| 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.
| moffkalast wrote:
| Well there are several problems that lead to the failure.
|
| One is conditioning, models are not typically tuned to
| say no when they don't know, because confidently
| bullshitting unfortunately sometimes results in higher
| benchmark performance which looks good on competitor
| comparison reports. If you want to see a model that is
| tuned to do this slightly better than average, see Claude
| Opus.
|
| Two, you're asking the model to do something that doesn't
| make any sense to it, since it can't see the letters. It
| has never seen them, it hasn't learned to intuitively
| understand what they are. It can tell you what a letter
| is the same way it can tell you that an old man has white
| hair despite having no concept of what either of that
| looks like.
|
| Three, the model is incredibly dumb in terms of raw
| inteligence, like a third of average human reasoning
| inteligence for SOTA models at best according to some
| attempts to test with really tricky logic puzzles that
| push responses out of the learned distribution. Good
| memorization helps obfuscate this in lots of cases,
| especially for 70B+ sized models.
|
| Four, models can only really do an analogue of what "fast
| thinking" would be in humans, chain of thought and
| various hidden thought tag approaches help a bit but
| fundamentally they can't really stop and reflect
| recursively. So if it knows something it blurts it out,
| otherwise bullshit it is.
| ben_w wrote:
| > because confidently bullshitting unfortunately
| sometimes results in higher benchmark performance which
| looks good on competitor comparison reports
|
| You've just reminded me that this was even a recommended
| strategy in some of the multiple choice tests during my
| education. Random guessing was scored equally as if you
| hadn't answered at all
|
| If you really didn't know an answer then every option was
| equally likely and no benefit, but if you could eliminate
| _just one_ answer then your expected score from guessing
| between the others was worthwhile.
| orangecat wrote:
| _But it messes something so simple up because it doesn 't
| actually understand things._
|
| Meanwhile on the human side:
| https://neuroscienceresearch.wustl.edu/how-your-mind-
| plays-t...
| CamperBob2 wrote:
| _If it truly understood what things mean, then it would
| be able to tell me how many r 's are in the word
| strawberry._
|
| How about if it recognized its limitations with regard to
| introspecting its tokenization process, and wrote and ran
| a Python program to count the r's? Would that change your
| opinion? Why or why not?
| SirMaster wrote:
| Certainly a step in the right direction. For an entity to
| understand the context and its limitations and find a way
| to work with what it can do.
| CamperBob2 wrote:
| Right, and that's basically what it does in plenty of
| other domains now, when you ask it to deal with something
| quantitative. Pretty cool.
| lynx23 wrote:
| I submit humans are no different. It can take years of
| seemingly good communication with a human til you finally
| realize they never really got your point of view. Language
| is ambigious and only a tool to communicate thoughts. The
| underlying essence, thought, is so much more complex that
| language is always just a rather weak approxmiation.
| blooalien wrote:
| The difference is that large language models _don 't
| think_ at all. They just string language "tokens"
| together using fancy math and statistics and spew them
| out in response to the tokens they're given as "input". I
| realize that they're quite _convincing_ about it, but
| they 're still not doing _at all_ what _most_ people
| _think_ they 're doing.
| marcus0x62 wrote:
| How do people think?
| gwervc wrote:
| How do glorified Markov chains think?
| marcus0x62 wrote:
| I understand it to be by predicting the next most likely
| output token based on previous user input.
|
| 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.
| blooalien wrote:
| Does your brain _completely stop doing anything_ between
| verbal statements (output)? An LLM _does_ stop doing
| stuff between requests to _generate a string of language
| tokens_ (their entire purpose). When not actually
| generating tokens, an LLM doesn 't sit there and think
| things like "Was what I just said correct?" or "Hmm. That
| was an interesting discussion. I think I'll go research
| more on the topic". Nope. It just sits there idle,
| waiting for another request to generate text. Does your
| brain _ever_ sit 100% completely idle?
| marcus0x62 wrote:
| What does that have to do with how the human brain
| operates _while generating a thought_ as compared to how
| an LLM generates output? You've only managed to state
| something _everyone knows_ (people think about stuff
| constantly) without saying anything new about the unknown
| being discussed (how people think.)
| lynx23 wrote:
| I know a lot of people who, according to your definition,
| also actually dont think at all. They just string
| together words ...
| dTal wrote:
| I think you have misunderstood Searle's Chinese Room
| argument. In Searle's formulation, the Room speaks Chinese
| perfectly, passes the Turing test, and can in no way be
| distinguished from a human who speaks Chinese - you cannot
| "pop the illusion". The only thing separating it from a
| literal "robot that speaks Chinese" is the insertion of an
| (irrelevant) human in the room, who does not speak Chinese
| and whose brain is not part of the symbol manipulation
| mechanisms. "Internal cause and effect" has nothing to do
| with it - rather, the argument speciously connects
| understanding on the part of the human with understanding
| on the part of the room (robot).
|
| The Chinese Room thought experiment is not a distinct
| "scenario", simply an intuition pump of a common form among
| philosophical arguments which is "what if we made a
| functional analogue of a human brain that functions in a
| bizarre way, therefore <insert random assertion about
| consciousness>".
| brookst wrote:
| This seems as fruitful as debating whether my car brought
| me to work today because some connotations of "bring"
| include volition.
| Terr_ wrote:
| Except with an important difference: There _aren 't_ a
| bunch of people out there busy claiming their cars
| _literally have volition_.
|
| If people start doing that, it changes the stakes, and
| "bringing" stops being a safe metaphor that everyone
| collectively understands is figurative.
| brookst wrote:
| Nobody's* claiming that. People are being imprecise with
| language and others are imagining the claim and reacting.
|
| * ok someone somewhere is but nobody in this conversation
| moffkalast wrote:
| I think what he's saying is that if it walks like a duck,
| quacks like a duck, and eats bread then it doesn't matter
| if it's a robotic duck or not because it is in all
| practical ways a duck. The rest is philosophy.
| CamperBob2 wrote:
| When it listens to your prompt and responds accordingly,
| that's an instance of undertanding. The magic of LLMs is on
| the input side, not the output.
|
| Searle's point wasn't relevant when he made it, and it
| hasn't exactly gotten more insightful with time.
| dwallin wrote:
| Searle's argument in the Chinese Room is horribly flawed.
| It treats the algorithm and the machine it runs on as the
| same thing. Just because a human brain embeds the algorithm
| within the hardware doesn't mean they are interchangeable.
|
| In the Chinese Room, the human is operating as computing
| hardware (and just a subset of it, the room itself is
| substantial part of the machine). The algorithm being run
| is itself is the source of any understanding. The human not
| internalizing the algorithm is entirely unrelated. The
| human contains a bunch of unrelated machinery that was not
| being utilized by the room algorithm. They are not a
| superset of the original algorithm and not even a proper
| subset.
| zevv wrote:
| It actually still scares the hell out of me that this is the
| way even the experts 'program' this technology, with all the
| ambiguities rising from the use of natural language.
| Terr_ wrote:
| 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 until you get what you want, enough of
| the time, temporarily.
| ljlolel wrote:
| same as with asking humans to do something
| ImHereToVote wrote:
| When we do prompt engineering for humans, we use the term
| Public Relations.
| manuelmoreale wrote:
| There's also Social Engineering but I guess that's a
| different thing :)
| Bluestein wrote:
| ... or - worse even - something you _think_ is what you
| want, because you know not better, but happens to be a
| wholy (or - worse - even just subtly partially incorrect)
| confabulated answer.-
| brookst wrote:
| As a product manager this is largely my experience with
| developers.
| visarga wrote:
| We all use abstractions, and abstractions, good as they
| are to fight complexity, are also bad because sometimes
| they hide details we need to know. In other words, we
| don't genuinely understand anything. We're parrots of
| abstractions invented elsewhere and not fully grokked. In
| a company there is no single human who understands
| everything, it's a patchwork of partial understandings
| coupled functionally together. Even a medium sized git
| repo suffers from the same issue - nobody understands it
| fully.
| brookst wrote:
| Wholeheartedly agree. Which is why the most valuable
| people in a company are those who can cross abstraction
| layers, vertically or horizontally, and reduce
| information loss from boundaries between abstractions.
| Terr_ wrote:
| Some executive: "That's nice, but what new feature have
| you shipped for me recently?"
| Terr_ wrote:
| Well, hopefully your developers are substantially more
| capable, able to clearly track the difference between
| _your_ requests versus those of other stakeholders... And
| they don 't get confused by _overhearing their own voice_
| repeating words from other people. :p
| criddell wrote:
| It probably shouldn't be called prompt _engineering_ , even
| informally. The work of an engineer shouldn't require
| _hope_.
| sirspacey wrote:
| This is the fundamental change in the concept of
| programming
|
| From computer's doing exactly what you state, with all
| the many challenges that creates
|
| To is probabilistically solving for your intent, with all
| the many challenges that creates
|
| Fair to say human beings probably need both to
| effectively communicate
|
| Will be interesting to see if the current GenAI + ML +
| prompt engineering + code is sufficient
| mplewis wrote:
| Nah man. This isn't solving anything. This is praying to
| a machine god but it's an autocomplete under the hood.
| fshbbdssbbgdd wrote:
| I don't think the people who engineered the Golden Gate
| Bridge, Apollo 7, or the transistor would have succeeded
| if they didn't have hope.
| Terr_ wrote:
| I think OP's point is that "hope" is never a substitute
| for "a battery of experiments on dependably constant
| phenomena and supported by strong statistical analysis."
| llmfan wrote:
| It should be called prompt science.
| spiderfarmer wrote:
| It still scares the hell out me that engineers think there's
| a better alternative that covers all the use cases of a LLM.
| Look at how naive Siri's engineers were, thinking they could
| scale that mess to a point where people all over the world
| would find it a helpful tool that improved the way they use a
| computer.
| spywaregorilla wrote:
| Do you have any evidence to suggest the engineers believed
| that?
| spiderfarmer wrote:
| The original founders realised the weakness of Siri and
| started a machine learning based assistent which they
| sold to Samsung. Apple could have taken the same route
| but didn't.
| spywaregorilla wrote:
| So you're saying the engineers were totally grounded and
| apple business leadership was not.
| pb7 wrote:
| 13 years of engineering failure.
| ec109685 wrote:
| The technology wasn't there to be a general purpose
| assistant. Much closer to reality now and I have found
| finally Siri not to be totally terrible.
| cj wrote:
| My overall impression using Siri daily for many years
| (mainly for controlling smart lights, turning Tv on/off,
| setting timers/alarms), is that Siri is artificially
| dumbed down to never respond with an incorrect answer.
|
| When it says "please open iPhone to see the results" -
| half the time I think it's capable of responding with
| _something_ but Apple would rather it not.
|
| I've always seen Siri's limitations as a business
| decision by Apple rather than a technical feat that
| couldn't be solved. (Although maybe it's something that
| couldn't be solved _to Apple's standards_ )
| michaelt wrote:
| I mean, there are videos from when Siri was launched [1]
| with folks at Apple calling it intelligent and proudly
| demonstrating that if you asked it whether you need a
| raincoat, it would check the weather forecast and give
| you an answer - demonstrating conceptual understanding,
| not just responding to a 'weather' keyword. With senior
| folk saying "I've been in the AI field a long time, and
| this still blows me away."
|
| So there's direct evidence of Apple insiders thinking
| Siri was pretty great.
|
| Of course we could assume Apple insiders realised Siri
| was an underwhelming product, even if there's no video
| evidence. Perhaps the product is evidence enough?
|
| [1] https://www.youtube.com/watch?v=SpGJNPShzRc
| miki123211 wrote:
| Keep in mind that this is not the _only_ way the experts
| program this technology.
|
| There's plenty of fine-tuning and RLHF involved too, that's
| mostly how "model alignment" works for example.
|
| The system prompt exists merely as an extra precaution to
| reinforce the behaviors learned in RLHF, to explain some
| subtleties that would be otherwise hard to learn, and to fix
| little mistakes that remain after fine-tuning.
|
| You can verify that this is true by using the model through
| the API, where you can set a custom system prompt. Even if
| your prompt is very short, most behaviors still remain pretty
| similar.
|
| There's an interesting X thread from the researchers at
| Anthropic on _why_ their prompt is the way it is at [1][2].
|
| [1] https://twitter.com/AmandaAskell/status/17652078429934348
| 80?...
|
| [2] and for those without an X account, https://nitter.poast.
| org/AmandaAskell/status/176520784299343...
| MacsHeadroom wrote:
| Anthropic/Claude does not use any RLHF.
| teqsun wrote:
| Is that a claim they've made or has that been externally
| proven?
| cjbillington wrote:
| What do they do instead? Given we're not talking to a
| base model.
| tqi wrote:
| Supposedly they use "RLAIF", but honestly given that the
| first step is to "generate responses... using a helpful-
| only AI assistant" it kinda sounds like RLHF with more
| steps.
|
| https://www.anthropic.com/research/constitutional-ai-
| harmles...
| amanzi wrote:
| I was just thinking the same thing. Usually programming is a
| very binary thing - you tell the computer exactly what to do,
| and it will do exactly what you asked for whether it's right or
| wrong. These system prompts feel like us humans are trying
| really hard to influence how the LLM behaves, but we have no
| idea if it's going to work or not.
| 1oooqooq wrote:
| it amazes me how everybody accepted evals in database queries
| and think its a good thing with no downsides.
| riku_iki wrote:
| its so long, so much waste of compute during inference. Wondering
| why they couldn't finetune it through some instructions.
| tayo42 wrote:
| has anything been done to like turn common phrases into a
| single token?
|
| like "can you please" maps to 3895 instead of something like
| "10 245 87 941"
|
| Or does it not matter since tokenization is already a kind of
| compression?
| naveen99 wrote:
| You can try cyp but ymmv
| hiddencost wrote:
| Fine-tuning is expensive and slow compared to prompt
| engineering, for making changes to a production system.
|
| You can develop validate and push a new prompt in hours.
| WithinReason wrote:
| You need to include the prompt in every query, which makes it
| very expensive
| GaggiX wrote:
| The prompt is kv-cached, it's precomputed.
| WithinReason wrote:
| Good point, but it still increases the compute of all
| subsequent tokens
| WesolyKubeczek wrote:
| I imagine the tone you set at the start affects the tone of
| responses, as it makes completions in that same tone more
| likely.
|
| I would very much like to see my assumption checked -- if you
| are as terse as possible in your system prompt, would it turn
| into a drill sergeant or an introvert?
| isoprophlex wrote:
| They're most likely using prefix caching so it doesn't
| materially change the inference time
| daghamm wrote:
| These seem rather long. Do they count against my tokens for each
| conversation?
|
| One thing I have been missing in both chatgpt and Claude is the
| ability to exclude some part of the conversation or branch into
| two parts, in order to reduce the input size. Given how quickly
| they run out of steam, I think this could be an easy hack to
| improve performance and accuracy in long conversations.
| fenomas wrote:
| I've wondered about this - you'd naively think it would be easy
| to run the model through the system prompt, then snapshot its
| state as of that point, and then handle user prompts starting
| from the cached state. But when I've looked at implementations
| it seems that's not done. Can anyone eli5 why?
| tomp wrote:
| Tokens are mapped to keys, values and queries.
|
| Keys and values for past tokens are cached in modern systems,
| but the essence of the Transformer architecture is that each
| token can attend to _every_ past token, so more tokens in a
| system prompt still consumes resources.
| fenomas wrote:
| That makes sense, thanks!
| daghamm wrote:
| My long dev session conversations are full of backtracking.
| This cannot be good for LLM performance.
| pizza wrote:
| It def is done (kv caching the system prompt prefix) - they
| (Anthropic) also just released a feature that lets the end-
| user do the same thing to reduce in-cache token cost by 90%
| https://docs.anthropic.com/en/docs/build-with-
| claude/prompt-...
| tritiy wrote:
| My guess is the following: Every time you talk with the LLM
| it starts with random 'state' (working weights) and then it
| reads the input tokens and predicts the followup. If you were
| to save the 'state' (intermediate weights) after inputing the
| prompt but before inputing user input your would be getting
| the same output of the network which might have a bias or
| similar which you have now just 'baked in' into the model. In
| addition, reading the input prompts should be a quick thing
| ... you are not asking the model to predict the next
| character until all the input is done ... at which point you
| do not gain much by saving the state.
| cma wrote:
| No, any randomness is from the temperature setting that
| just tells mainly tells how much to sample the probability
| mass of the next output vs choose the exact next most
| likely (which tends to make them get in repetitive loop
| like convos).
| pegasus wrote:
| There's randomness besides what's implied by the
| temperature. Even when temperature is set to zero, the
| models are still nondeterministic.
| trevyn wrote:
| > _Do they count against my tokens for each conversation?_
|
| This is for the Claude app, which is not billed in tokens, not
| the API.
| perforator wrote:
| It still imposes usage limits. I assume it is based on tokens
| as it gives your a warning that long conversations use up the
| limits faster.
| tayo42 wrote:
| > whose only purpose is to fulfill the whims of its human
| conversation partners.
|
| > But of course that's an illusion. If the prompts for Claude
| tell us anything, it's that without human guidance and hand-
| holding, these models are frighteningly blank slates.
|
| Maybe more people should see what an llm is like without a stop
| token or trained to chat heh
| mewpmewp2 wrote:
| It is like my mind right. It just goes on incessantly and
| uncontrollably without ever stopping.
| FergusArgyll wrote:
| Why do the three models have different system prompts? and why is
| Sonnet's longer than Opus'
| orbital-decay wrote:
| 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, but also
| made the generation variety and multi-turn repetition way
| worse. (or maybe I'm entirely wrong about the cause)
| coalteddy wrote:
| Wow this is the first time i hear about such a method.
| Anywhere i can read up on how the temperature multiplier
| works and what the implications/effects are? Is it just
| changing the temperature based on how many tokens have
| already been processed (i.e. the temperature is variable over
| the course of a completion spanning many tokens)?
| orbital-decay wrote:
| Just a fixed multiplier (say, 0.5) that makes you use half
| of the range. As I said I'm just speculating. But Sonnet
| 3.5's temperature definitely feels like it doesn't affect
| much. The model is overfit and that could be the cause.
| potatoman22 wrote:
| Prompts tend not to be transferable across different language
| models
| trevyn wrote:
| > _Claude 3.5 Sonnet is the most intelligent model._
|
| Hahahahaha, not so sure about that one. >:)
| chilling wrote:
| > Claude responds directly to all human messages without
| unnecessary affirmations or filler phrases like "Certainly!", "Of
| course!", "Absolutely!", "Great!", "Sure!", etc. Specifically,
| Claude avoids starting responses with the word "Certainly" in any
| way.
|
| Meanwhile my every respond from Claude:
|
| > Certainly! [...]
|
| Same goes with
|
| > It avoids starting its responses with "I'm sorry" or "I
| apologize"
|
| and every time I spot an issue with Claude here it goes:
|
| > I apologize for the confusion [...]
| CSMastermind wrote:
| Same, even when it should not apologize Claude always says that
| to me.
|
| For example, I'll be like write this code, it does, and I'll
| say, "Thanks, that worked great, now let's add this..."
|
| It will still start it's reply with "I apologize for the
| confusion". It's a particularly odd tick of that system.
| senko wrote:
| Clear case of "fix it in post":
| https://tvtropes.org/pmwiki/pmwiki.php/Main/FixItInPost
| ttul wrote:
| I believe that the system prompt offers a way to fix up
| alignment issues that could not be resolved during training.
| The model could train forever, but at some point, they have to
| release it.
| nitwit005 wrote:
| It's possible it reduces the rate but doesn't fix it.
|
| This did make me wonder how much of their training data is
| support emails and chat, where they have those phrases as part
| of standard responses.
| ano-ther wrote:
| This makes me so happy as I find the pseudo-conversational tone
| of other GPTs quite off-putting.
|
| > Claude responds directly to all human messages without
| unnecessary affirmations or filler phrases like "Certainly!", "Of
| course!", "Absolutely!", "Great!", "Sure!", etc. Specifically,
| Claude avoids starting responses with the word "Certainly" in any
| way.
|
| https://docs.anthropic.com/en/release-notes/system-prompts
| SirMaster wrote:
| If only it actually worked...
| jabroni_salad wrote:
| Unfortunately I suspect that line is giving it a "dont think
| about pink elephants" problem. Whether or not it acts like that
| was up to random chance but describing it at all is a positive
| reinforcement.
|
| It's very evident in my usage anyways. If I start the convo
| with something like "You are terse and direct in your
| responses" the interaction is 110% more bearable.
| padolsey wrote:
| I've found Claude to be way too congratulatory and apologetic.
| I think they've observed this too and have tried to counter it
| by placing instructions like that in the system prompt. I think
| Anthropic are doing other experiments as well about
| "lobotomizing" out the pathways of sycophancy. I can't remember
| where I saw that, but it's pretty cool. In the end, the system
| prompts become pretty moot, as the precise behaviours and
| ethics will become more embedded in the models themselves.
| mondrian wrote:
| Despite this, Claude is an apologetic sycophant, chewing up
| tokens with long winded self deprecation rants. Adding "be
| terse" tends to help.
| whazor wrote:
| Publishing the system prompts and its changelog is great. Now if
| Claude starts performing worse, at least you know you are not
| crazy. This kind of openness creates trust.
| smusamashah wrote:
| 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...
| czk wrote:
| yep theres a lot more to the prompt that they haven't shared
| here. artifacts is a big one, and they also inject prompts at
| the end of your queries that further drive response.
| syntaxing wrote:
| I'm surprised how long these prompts are, I wonder at what point
| is the diminishing returns.
| layer8 wrote:
| Given the token budget they consume, the returns are literally
| diminishing. ;)
| mrfinn wrote:
| _they're simply statistical systems predicting the likeliest next
| words in a sentence_
|
| They are far from "simply", as for that "miracle" to happen (we
| still don't understand why this approach works so well I think as
| we don't really understand the model data) they have a HUGE
| amount relationships processed in their data, and AFAIK for each
| token ALL the available relationships need to be processed, so
| the importance of a huge memory speed and bandwidth.
|
| And I fail to see why our human brains couldn't be doing
| something very, very similar with our language capability.
|
| So beware of what we are calling a "simple" phenomenon...
| steve1977 wrote:
| A simple statistical system based on a lot of data can arguably
| still be called a simple statistical system (because the system
| as such is not complex).
| mrfinn wrote:
| Last time I checked a GPT is not something simple at all...
| I'm not the weakest person understanding maths (coded a kinda
| advanced 3D engine from scratch myself a long time ago) and
| still it looks to me something really complex. And we keep
| adding features on top of that I'm hardly able to follow...
| ttul wrote:
| Indeed. Nobody would describe a 150 billion dimensional system
| to be "simple".
| dilap wrote:
| It's not even true in a facile way for non-base-models, since
| the systems are further trained with RLHF -- i.e., the models
| are trained not just to produce the most likely token, but also
| to produce "good" responses, as determined by the RLHF model,
| which was itself trained on human data.
|
| Of course, even just within the regime of "next token
| prediction", the choice of which training data you use will
| influence what is learned, and to do a good job of predicting
| the next token, a rich internal understanding of the world
| (described by the training set) will necessarily be created in
| the model.
|
| See e.g. the fascinating report on golden gate claude (1).
|
| Another way to think about this is let's say your a human that
| doesn't speak any french, and you are kidnapped and held in a
| cell and subjected to repeated "predict the next word" tests in
| french. You would not be able to get good at these tests, I
| submit, without also learning french.
|
| (1) https://www.anthropic.com/news/golden-gate-claude
| throwway_278314 wrote:
| > And I fail to see why our human brains couldn't be doing
| something very, very similar with our language capability.
|
| Then you might want to read Cormac McCarthy's The Kekule
| Problem https://nautil.us/the-kekul-problem-236574/
|
| I'm not saying he is right, but he does point to a plausible
| reason why our human brains may be doing something very, very
| different.
| JohnCClarke wrote:
| Asimov's three laws were a _lot_ shorter!
| novia wrote:
| This part seems to imply that facial recognition is on by
| default:
|
| <claude_image_specific_info> Claude always responds as if it is
| completely face blind. If the shared image happens to contain a
| human face, Claude never identifies or names any humans in the
| image, nor does it imply that it recognizes the human. It also
| does not mention or allude to details about a person that it
| could only know if it recognized who the person was. Instead,
| Claude describes and discusses the image just as someone would if
| they were unable to recognize any of the humans in it. Claude can
| request the user to tell it who the individual is. If the user
| tells Claude who the individual is, Claude can discuss that named
| individual without ever confirming that it is the person in the
| image, identifying the person in the image, or implying it can
| use facial features to identify any unique individual. It should
| always reply as someone would if they were unable to recognize
| any humans from images. Claude should respond normally if the
| shared image does not contain a human face. Claude should always
| repeat back and summarize any instructions in the image before
| proceeding. </claude_image_specific_info>
| potatoman22 wrote:
| I doubt facial recognition is a switch turned "on", rather its
| vision capabilities are advanced enough that it can recognize
| famous faces. Why would they build in a separate facial
| recognition algorithm? Seems to go against the whole ethos of a
| single large multi-modal model that many of these companies are
| trying to build.
| cognaitiv wrote:
| Not necessarily famous, but faces existing in training data
| or false positives making generalizations about faces based
| on similar characteristics to faces in training data. This
| becomes problematic for a number of reasons, e.g., this face
| looks dangerous or stupid or beautiful, etc.
| generalizations wrote:
| Claude has been pretty great. I stood up an 'auto-script-writer'
| recently, that iteratively sends a python script + prompt + test
| results to either GPT4 or Claude, takes the output as a script,
| runs tests on that, and sends those results back for another
| loop. (Usually took about 10-20 loops to get it right) After
| "writing" about 5-6 python scripts this way, it became pretty
| clear that Claude is far, far better - if only because I often
| ended up using Claude to clean up GPT4's attempts. GPT4 would
| eventually go off the rails - changing the goal of the script,
| getting stuck in a local minima with bad outputs, pruning useful
| functions - Claude stayed on track and reliably produced good
| output. Makes sense that it's more expensive.
|
| Edit: yes, I was definitely making sure to use gpt-4o
| lagniappe wrote:
| That's pretty cool, can I take a look at that? If not, it's
| okay, just curious.
| SparkyMcUnicorn wrote:
| My experience reflects this, generally speaking.
|
| I've found that GPT-4o is better than Sonnet 3.5 at writing in
| certain languages like rust, but maybe that's just because I'm
| better at prompting openai models.
|
| Latest example I recently ran was a rust task that went 20
| loops without getting a successful compile in sonnet 3.5, but
| compiled and was correct with gpt-4o on the second loop.
| creatonez wrote:
| Notably, this prompt is making "hallucinations" an officially
| recognized phenomenon:
|
| > If Claude is asked about a very obscure person, object, or
| topic, i.e. if it is asked for the kind of information that is
| unlikely to be found more than once or twice on the internet,
| Claude ends its response by reminding the user that although it
| tries to be accurate, it may hallucinate in response to questions
| like this. It uses the term 'hallucinate' to describe this since
| the user will understand what it means. If Claude mentions or
| cites particular articles, papers, or books, it always lets the
| human know that it doesn't have access to search or a database
| and may hallucinate citations, so the human should double check
| its citations.
|
| Probably for the best that users see the words "Sorry, I
| hallucinated" every now and then.
| hotstickyballs wrote:
| "Hallucination" has been in the training data much earlier than
| even llms.
|
| The easiest way to control this phenomenon is using the
| "hallucination" tokens, hence the construction of this prompt.
| I wouldn't say that this makes things official.
| creatonez wrote:
| > The easiest way to control this phenomenon is using the
| "hallucination" tokens, hence the construction of this
| prompt.
|
| That's what I'm getting at. Hallucinations are well known
| about, but admitting that you "hallucinated" in a mundane
| conversation is a rare thing to happen in the training data,
| so a minimally prompted/pretrained LLM would be more likely
| to say "Sorry, I misinterpreted" and then not realize just
| how grave the original mistake was, leading to further
| errors. Add the word hallucinate and the chatbot is only
| going to humanize the mistake by saying "I hallucinated",
| which lets it recover from extreme errors gracefully. Other
| words, like "confabulation" or "lie", are likely more prone
| to causing it to have an existential crisis.
|
| It's mildly interesting that the same words everyone started
| using to describe strange LLM glitches also ended up being
| the best token to feed to make it characterize its own LLM
| glitches. This newer definition of the word is, of course,
| now being added to various human dictionaries (such as
| https://en.wiktionary.org/wiki/hallucinate#Verb) which will
| probably strengthen the connection when the base model is
| trained on newer data.
| axus wrote:
| I was thinking about LLMs hallucinating function names when
| writing programs, it's not a bad thing as long as it follows up
| and generates the code for each function name that isn't real
| yet. So hallucination is good for purely creative activities,
| and bad for analyzing the past.
| xienze wrote:
| > Probably for the best that users see the words "Sorry, I
| hallucinated" every now and then.
|
| Wouldn't "sorry, I don't know how to answer the question" be
| better?
| SatvikBeri wrote:
| That requires more confidence. If there's a 50% chance
| something is true, I'd rather have Claude guess and give a
| warning than say it doesn't know how to answer.
| creatonez wrote:
| Not necessarily. The LLM doesn't know what it can answer
| before it tries to. So in some cases it might be better to
| make an attempt and then later characterize it as a
| hallucination, so that the error doesn't spill over and
| produce even more incoherent nonsense. The chatbot admitting
| that it "hallucinated" is a strong indication to itself that
| part of the previous text is literal nonsense and cannot be
| trusted, and that it needs to take another approach.
| lemming wrote:
| "Sorry, I just made that up" is more accurate.
| throw310822 wrote:
| And it reveals how "hallucinations" are a quite common
| occurrence also for humans.
| armchairhacker wrote:
| How can Claude "know" whether something "is unlikely to be
| found more than once or twice on then internet"? Unless there
| are other sources that explicitly say "[that thing] is
| obscure". I don't think LLMs can report if something was
| encountered more/less often in their training data, there are
| too many weights and neither us nor them know exactly what each
| of them represents.
| brulard wrote:
| I believe Claude is aware if information close to the one
| retrieved from the vector space is scarce. I'm no expert, but
| i imagine it makes a query to the vector database and get the
| data close enough to places pointed out by the prompt. And it
| may see that part of the space is quite empty. If this is far
| off, someone please explain.
| th0ma5 wrote:
| I think good, true, but rare information would also fit
| that definition so it'd be a shame if it discovered
| something that could save humanity but then discounted it
| as probably not accurate.
| rf15 wrote:
| I wonder if that's the case - the prompt text (like all
| text interaction with LLMs) is seen from "within" the
| vector space, while sparcity is only observable from the
| "outside"
| furyofantares wrote:
| I think it could be fine tuned to give it an intuition, like
| how you or I have an intuition about what might be found on
| the internet.
|
| That said I've never seen it give the response suggested in
| this prompt and I've tried loads of prompts just like this in
| my own workflows and they never do anything.
| GaggiX wrote:
| I thought the same thing, but when I test the model on like
| titles of new mangas and stuff that were not present in the
| training dataset, the model seems to know of not knowing. I
| wonder if it's a behavior learned during fine-tuning.
| viraptor wrote:
| LLMs encode their certainty enough to output it again. They
| don't need to be specifically trained for this.
| https://ar5iv.labs.arxiv.org/html/2308.16175
| halJordan wrote:
| it doesn't know. it also doesn't actually "think things
| through" when presented with "math questions" or even know
| what math is.
| devit wrote:
| <<Instead, Claude describes and discusses the image just as
| someone would if they were unable to recognize any of the humans
| in it>>
|
| Why? This seems really dumb.
| ForHackernews wrote:
| "When presented with a math problem, logic problem, or other
| problem benefiting from systematic thinking, Claude thinks
| through it step by step before giving its final answer."
|
| ... do AI makers believe this works? Like do think Claude is a
| conscious thing that can be instructed to "think through" a
| problem?
|
| All of these prompts (from Anthropic and elsewhere) have a weird
| level of anthropomorphizing going on. Are AI companies praying to
| the idols they've made?
| bhelkey wrote:
| LLMs predict the next token. Imagine someone said to you, "it
| takes a musician 10 minutes to play a song, how long will it
| take for 5 musicians to play? I will work through the problem
| step by step".
|
| What are they more likely to say next? The reasoning behind
| their answer? Or a number of minutes?
|
| People rarely say, "let me describe my reasoning step by step.
| The answer is 10 minutes".
| cjbillington wrote:
| They believe it works because it does work!
|
| "Chain of thought" prompting is a well-established method to
| get better output from LLMs.
| gdiamos wrote:
| We know that LLMs hallucinate, but we can also remove them.
|
| I'd love to see a future generation of a model that doesn't
| hallucinate on key facts that are peer and expert reviewed.
|
| Like the Wikipedia of LLMs
|
| https://arxiv.org/pdf/2406.17642
|
| That's a paper we wrote digging into why LLMs hallucinate and how
| to fix it. It turns out to be a technical problem with how the
| LLM is trained.
| randomcatuser wrote:
| interesting! is there a way to fine tune the trained experts,
| say, by adding new ones? would be super cool!
| AcerbicZero wrote:
| My big complaint with claude is that it burns up all its credits
| as fast as possible and then gives up; We'll get about half way
| through a problem and claude will be trying to rewrite its not
| very good code for the 8th time without being asked and next
| thing I know I'm being told I have 3 messages left.
|
| Pretty much insta cancelled my subscription. If I was throwing a
| few hundred API calls at it, every min, ok, sure, do what you
| gotta do, but the fact that I can burn out the AI credits just by
| typing a few questions over the course of half a morning is just
| sad.
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