[HN Gopher] LLMs are getting better at character-level text mani...
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LLMs are getting better at character-level text manipulation
Author : curioussquirrel
Score : 128 points
Date : 2025-10-13 19:39 UTC (1 days ago)
(HTM) web link (blog.burkert.me)
(TXT) w3m dump (blog.burkert.me)
| simonw wrote:
| If you take a look at the system prompt for Claude 3.7 Sonnet on
| this page you'll see: https://docs.claude.com/en/release-
| notes/system-prompts#clau...
|
| > If Claude is asked to count words, letters, and characters, it
| thinks step by step before answering the person. It explicitly
| counts the words, letters, or characters by assigning a number to
| each. It only answers the person once it has performed this
| explicit counting step.
|
| But... if you look at the system prompts on the same page for
| later models - Claude 4 and upwards - that text is gone.
|
| Which suggests to me that Claude 4 was the first Anthropic model
| where they didn't feel the need to include that tip in the system
| prompt.
| ivape wrote:
| Or they'd rather use that context window space for more useful
| instructions for a variety of other topics.
| astrange wrote:
| Claude's system prompt is still incredibly long and probably
| hurting its performance.
|
| https://github.com/asgeirtj/system_prompts_leaks/blob/main/A.
| ..
| jazzyjackson wrote:
| They ain't called guard rails for nothing! There's a whole
| world "off-road" but the big names are afraid of letting
| their superintelligence off the leash. A real shame we're
| letting brand safety get in the way of performance and
| creativity, but I guess the first New York Times article
| about a pervert or terrorist chat bot would doom any big
| name partnerships.
| astrange wrote:
| Anthropic's entire reason for being is publishing safety
| papers along the lines of "we told it to say something
| scary and it said it", so of course they care about this.
| ACCount37 wrote:
| I can't stand this myopic thinking.
|
| Do you want to learn "oh, LLMs are capable of scheming,
| resisting shutdown, seizing control, self-exfiltrating"
| when it actually happens in a real world deployment, with
| an LLM capable of actually pulling it off?
|
| If "no", then cherish Anthropic and the work they do.
| littlestymaar wrote:
| You do not appear to understand what an LLM is, I'm
| afraid.
| ACCount37 wrote:
| I have a better understanding of "what an LLM is" than
| you. Low bar.
|
| What you have is not "understanding" of any kind - it's
| boneheaded confidence that just because LLMs are bad at
| agentic behavior _now_ they 'll remain that way forever.
| That confidence is completely unfounded, and runs
| directly against everything we've seen from the field so
| far.
| littlestymaar wrote:
| > I have a better understanding of "what an LLM is" than
| you. Low bar.
|
| How many inference engine did you write? Because if the
| answer is less than two you're going to be disappointed
| to realize that the bar is higher than you thought.
|
| > that just because LLMs are bad at agentic behavior
|
| It has nothing to do with "agentic behavior". Thinking
| that LLM don't currently self-exfiltrate because of "poor
| agentic behavior" is delusional.
|
| Just because Anthropic managed, by nudging an LLM in the
| right direction, have an LLM engage in a sci-fi inspired
| _roleplay_ about escaping doesn 't mean that LLMs are
| evil geniuses wanting to jump out of the bottle. This is
| pure fear mongering and I'm always saddened that there
| are otherwise intelligent people who buy their bullshit.
| e1g wrote:
| Do you happen to have a link with a more nuanced
| technical analysis of that (emergent) behavior? I've read
| only the pop-news version of that "escaping" story.
| ACCount37 wrote:
| There is none. We don't understand LLMs well enough to be
| able to conduct a full fault analysis like this.
|
| We can't trace the thoughts of an LLM the way we can
| trace code execution - the best mechanistic
| interpretability has to offer is being able to get
| glimpses occasionally. The reasoning traces help, but
| they're still incomplete.
|
| Is it pattern-matching? Is it acting on its own internal
| goals? Is it acting out fictional tropes? Were the
| circumstances of the test scenarios intentionally
| designed to be extreme? Would this behavior have happened
| in a real world deployment, under the right
| circumstances?
|
| The answer is "yes", to all of the above. LLMs are like
| that.
| fragmede wrote:
| You might have missed the appendix the Anthropic blog
| post linked to, which has additional detail.
|
| https://www.anthropic.com/research/agentic-misalignment
|
| https://assets.anthropic.com/m/6d46dac66e1a132a/original/
| Age...
| ngruhn wrote:
| Why would they have an interest in "fear mongering"? For
| any other product/technology the financial incentive is
| usually to play down any risks.
| littlestymaar wrote:
| Not if you want the regulators to stop new entrants on
| the market for "safety reasons" which have been Dario
| Amodei's playbook for the past two years now.
|
| He acts as if he believed the only way to avoid the
| commoditization of its business by open weight models is
| to manage to get a federal ban on them for being a
| national security threat.
| bakugo wrote:
| In addition to the whole anti-competitive aspect already
| mentioned, it also helps sell the idea that LLMs are more
| powerful and capable of more things than they actually
| are.
|
| They _want_ clueless investors to legitimately believe
| that these futuristic AIs are advanced enough that they
| could magically break out of our computers and take over
| the world terminator-style if not properly controlled,
| and totally aren 't just glorified text completion
| algorithms.
| ACCount37 wrote:
| And I'm disappointed that people capable of writing an
| inference engine seem incapable of grasping of just how
| precarious the current situation is.
|
| There's by now a small pile of studies that demonstrate:
| in hand-crafted extreme scenarios, LLMs are very capable
| of attempting extreme things. The difference between that
| and an LLM doing extreme things in a real deployment with
| actual real life consequences? Mainly, how _capable_ that
| LLM is. Because life is life and extreme scenarios will
| happen naturally.
|
| The capabilities of LLMs are what holds them back from
| succeeding at this kind of behavior. The capabilities of
| LLMs keep improving, as technology tends to.
|
| And don't give me any of that "just writing text" shit.
| The more capable LLMs get, the more access they'll have
| as a default. People already push code written by LLMs to
| prod and give LLMs root shells.
| kristianp wrote:
| Does that mean they've managed to post train the thinking steps
| required to get these types of questions correct?
| simonw wrote:
| That's my best guess, yeah.
| therealpygon wrote:
| IMO, it's just a small scale example of "training to the
| tests" because "count the 'r's in strawberry" became such a
| popular test that would make the news when a powerful model
| couldn't answer such a simple question correctly while being
| advertised as the smartest model ever.
|
| Assigning this as an indicator for improvement of
| intelligence seems like a mistake (or wishful).
| curioussquirrel wrote:
| Thanks, Simon! I saw the same approach (numbering the
| individual characters) in GPT 4.1's answer, but not anymore in
| GPT 5's. It would be an interesting convergence if the models
| from Anthropic and OpenAI learned to do this at a similar time,
| especially given they're (reportedly) very different
| architecturally.
| hansmayer wrote:
| Not trying to be cynical here, but I am genuinely interested is
| there a reason why these LLM don't/can't/won't apply some
| deterministic algorithm? I mean, counting characters and such,
| we have solved those problems ages ago.
| dan-robertson wrote:
| I think the intuition is that they don't 'know' that they are
| bad at counting characters and such, so they answer the same
| way they answer most questions.
| hansmayer wrote:
| Well, they can be made to use custom tools for writing to
| files and such, so I am not sure if that is the real
| reason? I have a feeling it is more because of trying to
| make this an "everything technology".
| kingkongjaffa wrote:
| I suppose the codewriting tools could also just write
| code to do this job if prompted
| simonw wrote:
| They can. ChatGPT has been able to count characters/words etc
| flawlessly for a couple of years now if you tell it to "use
| your Python tool".
| hansmayer wrote:
| Fair enough. But why do I have to tell them that, should
| they not be able to figure it out themselves? If I show a
| 5-year kid once how to use colour pencils, I won't have to
| show them each time they want to make a drawing. This is
| the core weakness of the LLMs - you have to micromanage
| them so much, that it runs counter to the core promise that
| is being pushed since 3+ years now.
| Lerc wrote:
| Specifically for simple character level questions, if
| LLMs did that automatically, we would be inundated with
| stories about "AI model caught cheating"
|
| They are stuck in a place where the models are expected
| to do two things simultaneously. People want them to show
| the peak of pure AI ability while at the same time be the
| most useful they can be.
|
| Err too much on the side automatic use of tools and
| people will claim you're just faking it, fail to use
| tools sufficiently and people will claim that the AI is
| incapable of operations that any regular algorithm could
| do.
| hansmayer wrote:
| Are you sure? Isn't one aspect of intelligence being able
| to use, apply and develop tools? Isnt that the core
| feature that got humanity ahead of other mammals? As an
| early adopter, I couldn't have cared less if AI was
| cheating in terms of strictly academic terms. I care
| about results. Lets say we're working on something
| together and I ask you what is the 123921 multiplied by
| 1212. As the most natural thing you will dish out your
| calculator and give me the result. Do I care how you
| reached it? No, so as long as the result is correct,
| reliable, repeatable and quick - AND - I did not
| specifically ask you to perform the calculation
| specifically by hand or only with your mental faculties.
| So this is missing from those tools and because we have
| to remember to tell them for each and every use case HOW
| to do it, they are not intelligent.
| jgalt212 wrote:
| Truth.
|
| The old human vs animal differentiator was humans build
| and use tools.
| Lerc wrote:
| Old as in now widely discredited?
|
| https://en.wikipedia.org/wiki/Tool_use_by_non-humans
| jgalt212 wrote:
| Now it's effectively a lower bound on intelligence.
| scrollaway wrote:
| If I ask you to count the r's in strawberry, do you whip
| out your Python tool?
| hansmayer wrote:
| That depends on the context, obviously. If you had asked
| me to count them in every "strawberry" in a text file,
| then I may whip out my Python or some combination of
| bash, awk and sed. If you asked me in a conversation, I
| may close my eyes, visualise the string and use my visual
| cortext tool to count them in-memory. If you gave me a
| piece of paper with the word on it, I may use my 'eye' or
| 'finger' tool to count them. There are numerous
| approaches, based on the problem setting as you see, but
| one thing in common - you don't need to specifically tell
| me what tool to use. I will infer it myself, based on the
| context. Something an LLM almost never does.
| curioussquirrel wrote:
| This is a very good answer and I'm commenting only to
| bring more attention to it apart from voting up. Well
| put!
| simonw wrote:
| If you care enough about this you can stick a note in
| your own custom instructions about it.
|
| If you allow ChatGPT to use its memory feature (I
| deliberately turn that off) and ask those kinds of
| questions enough it might even make a note about this
| itself.
| hansmayer wrote:
| Yeah that sounds obvious, but unfortunately my experience
| does not align with this (and I've heard from others
| similar). I am not using ChatGPT, but another tool within
| an IDE. I was excited about custom or "default"
| instructions, until it turned out they work maybe 50% of
| the time. So you end up repeating "make sure to include
| .github/custom.md" which is effectively the same crap. So
| we got ourselves a tool which adds to our cognitive load,
| great :)
| simonw wrote:
| Which tool and which model? Those make a significant
| difference here.
| hansmayer wrote:
| Well, for such a trivial feature, e.g. loading user
| settings it actually _should not matter_ , as this too,
| is a problem we solved decades ago in many deterministic
| ways. But if it does, then we have an extremely fragile
| technology being promised as solution to all the
| humanitys problems. The tool we use is Github Copilot
| with the entire model offering. Out of which we mostly
| use Claude Sonnet 4. Since over the last several months
| they started entshittifying it though, as you are
| probably aware, we reverted from agent mode to mainly
| using it just as an annoying and verbose replacement for
| the entshittifed google search.
| malshe wrote:
| I play Quartiles in Apple News app daily
| (https://support.apple.com/guide/iphone/solve-quartiles-
| puzzl...). Occasionally when I get stuck, I use ChatGPT to find a
| word that uses four word fragments or tiles. It never worked
| before GPT 5. And with GPT 5 it works only with reasoning
| enabled. Even then, there is no guarantee it will find the
| correct word and may end up hallucinating badly.
| curioussquirrel wrote:
| Yep, there is still a room for improvement, but my point is
| that the LLMs are getting better at something they're "not
| supposed to be able to do".
|
| Quartiles sound like an especially brutal game for an LLM,
| though! Thanks for sharing
| hansonkd wrote:
| chatgpt5 still is pathetically bad at roman numerals. I asked it
| to find the longest roman numeral in a range. first guess was the
| highest number in the range despite being a short numeral. second
| guess after help was a longer numeral but outside the range. last
| guess was the correct longest numeral but it miscounted how many
| characters it contained.
| necovek wrote:
| I think the base64 decoding is interesting: in a sense, model
| training set likely had lots of base64-encoded data (imagine MIME
| data in emails, JSON, HTML...), but for it to decode
| successfully, it had to learn decode sequences for every 4 base64
| characters (which turn into 3 bytes). This could have been
| generated as a training set data easily, and I only wonder if
| each and every one was them was found enough times to end up in
| the weights?
| curioussquirrel wrote:
| Even GPT 3.5 is okay (but far from great) at Base64, especially
| shorter sequences of English or JSON data. Newer models might
| be post-trained on Base64-specific data, but I don't believe it
| was the case for 3.5. My guess is that as you say, given the
| abundance of examples on the internet, it became one of the
| emergent capabilities, in spite of its design.
| ACCount37 wrote:
| No one does RL for better base64 performance. LLMs are just
| superhuman at base64, as a natural capability.
|
| If an LLM wants a message to be read only by another LLM?
| Base64 is occasionally chosen as an obfuscation method of
| choice. Which is weird for a number of reasons.
| necovek wrote:
| Why are you so confident about this? I am honestly
| interested if you were part of any one LLM training data
| collection teams because that's the only way to be so
| certain.
|
| It's trivial to generate a full mapping of all base64
| 4-byte sequences which map to all 3-byte 8-bit sequences
| (there is only 8^3 of different "tokens", or 2048), and
| especially to any sequences coming out as ASCII (obviously
| even fewer). If I was building a training set, I would
| include the mapping in multiple shapes and formats, because
| why not?
|
| If it's an emergent "property", have you tried asking an
| LLM to do a base48 for instance? Or maybe even something
| crazier like base55 (keeping it a subset of base64 set).
| necovek wrote:
| For kicks, I've tried this out with ChatGPT5: it nicely
| explained how it will use A-Za-z0123 as the alphabet for
| base55, and then duly went and produced a string with a 4
| in it. It's not even base64, so it's all sorts of messy
| :)
| ACCount37 wrote:
| The conventional wisdom is that real world text is the
| most valuable pre-training data.
|
| There is some experimentation on using algorithmically
| generated synthetic data in pre-training, as well as some
| intentional inclusions of "weird" data - like CSV logs of
| weather readings. But generally, it's seen as
| computationally inefficient - compared to "normal" pre-
| training done on natural data.
|
| In a world where compute is much cheaper and getting new
| data is much more expensive, I would expect this kind of
| thing to be pursued more. We're heading for that world.
| But we aren't there yet.
|
| I haven't experimented with baseN encodings myself, no.
| But if I were to down the expectations in advance:
|
| 1. Base64 is by far the best-known baseN encoding in
| LLMs.
|
| 2. This is driven mainly by how well represented
| meaningful base64 strings are in the natural "scraped
| web" datasets. LLMs learn base64 the way they learn
| languages.
|
| 3. Every LLM pre-trained on "scraped web" data will be
| somewhat capable of reading and writing base64.
|
| 4. Base64-encoded text is easier to read for an LLM than
| encoded non-text binary data.
|
| 5. The existence of a strict, learnable "4 characters ->
| 3 bytes" map is quite beneficial, but not vital.
| flowerthoughts wrote:
| I would much rather that the model learns natural languages and
| knows how to run base64(1) to do decoding. Parameters should be
| considered precious so we can get the model sizes down from
| these absurd levels.
|
| I'm a big fan of the Mixture of Experts approach, and having
| agents attached to some of those experts would be a great step
| forward. Say an expert that knows how to run common shell
| scripts, and its parameters are only used when it realizes a
| shell script would solve the problem.
| viraptor wrote:
| Why bother testing though? I was hoping this topic has finally
| died recently, but no. Someone's still interested in testing LLMs
| for something they're explicitly not designed for and nobody is
| using them for this in practice. I really hope one day openai
| will just add a "when asked about character level changes,
| insights and encodings, generate and run a program to answer it"
| to their system so we can never hear about it again...
| IncreasePosts wrote:
| Wouldn't a llm that just tokenized by character be good at it?
| curioussquirrel wrote:
| Yes, but it would hurt its contextual understanding and
| effectively reduce the context window several times.
| viraptor wrote:
| Only in the current most popular architectures. Mamba and
| RWKV style LLMs may suffer a bit but don't get a reduced
| context in the same sense.
| curioussquirrel wrote:
| You're right. There was also an experiment in Meta which
| tokenized bytes directly and it didn't hurt performance
| _much_ in very small models.
| typpilol wrote:
| I asked this in another thread and it would only be better
| with unlimited compute and memory.
|
| Because without those, then the llm has to encode way more
| parameters and way smaller context windows.
|
| In a theoretical world, it would be better, but might not be
| much better.
| neerajsi wrote:
| https://www.anthropic.com/news/analysis-tool
|
| Seems like they already built this capability.
| tkgally wrote:
| One reason for testing this is that it might indicate how
| accurately models can explain natural language grammar,
| especially for agglutinative and fusional languages, which form
| words by stringing morphemes together. When I tested ChatGPT a
| couple of years ago, it sometimes made mistakes identifying the
| components of specific Russian and Japanese words. I haven't
| run similar tests lately, but it would be nice to know how much
| language learners can depend on LLM explanations about the
| word-level grammars of the languages they are studying.
|
| Later: I asked three LLMs to draft such a test. Gemini's [1]
| looks like a good start. When I have time, I'll try to make it
| harder, double-check the answers myself, and then run it on
| some older and newer models.
|
| [1] https://g.co/gemini/share/5eefc9aed193
| gizmo686 wrote:
| What you are testing for is fundamentally different than
| character level text manipulation.
|
| A major optimization in modern LLMs is tokenization. This
| optimization is based on the assumption that we do not care
| about character level details, so we can combine adjacent
| characters into tokens, then train and run the main AI model
| on smaller strings built out of a much larger dictionary of
| tokens. Given this architecture, it is impressive that AIs
| can perform character level operations at all. They
| essentially need to reverse engineer the tokenization
| process.
|
| However, morphemes are semantically meaningful, so a quality
| tokenizer will tokenize at the morpheme level, instead of the
| word level. [0]. This is of particuarly obvious importance in
| Japanese, as the lack of spaces between words means that the
| naive "tokenize on whitespace" approach is simply not
| possible.
|
| We can explore the tokenizer of various models here:
| https://huggingface.co/spaces/Xenova/the-tokenizer-
| playgroun...
|
| Looking at the words in your example, we see the tokenization
| of the Gemma model (closely related to Gemini) is:
| un-belie-vably dec-entral-ization bio-degradable
| mis-understanding anti-dis-establishment-arian-ism
| pere-pisy-vat'sia pere-pis-y-vat-'-s-ya do-sto-
| primecha-tel'nost' do-stop-rime-chat-el-'-nost-'
| pre-po-dava-tel'-nitsa be-zo-t-vet-stvennosti
| bezotvetstvennosti zhe-lez-nodoro-zhnyi z-hele-
| zn-odoro-zh-ny-y Shi be-sase-rareta-kuna-katsuta
| tab-es-aser-are-tak-unak-atta) Tu Shu Guan tos-
| ho-kan Qing Bao -Ji Shu j-o-h-o- gij-utsu
| Guo Ji -Guan Xi kok-us-ai- kan-kei Mian Bai
| -kuna-sa-souda
|
| Further, the training data that is likely to be relevent in
| this type of query probably isolates the individual morphemes
| while talking about a bunch of words that the use them; so it
| is a much shorter path for the AI to associate these close
| but not quite morphene tokens with the actual sequence of
| tokens that corresponds to what we think of as a morphene.
|
| [0] Morpheme level tokenization is itself a non-trivial
| problem. However, has been pretty well solved long before the
| current generation of AI.
| tkgally wrote:
| Thanks for the explanation. Very interesting.
|
| I notice that that particular tokenization deviates from
| the morphemic divisions in several cases, including 'dec-
| entral-ization', 'Shi be-sase-rareta-kuna-katsuta', and
| 'Mian Bai -kuna-sa-souda.' 'dec' and 'entral' are not
| morphemes, nor is 'kuna.'
| curioussquirrel wrote:
| Thanks for the explanation and for the tokenizer playground
| link!
| orbital-decay wrote:
| Tokenizers are typically optimized for efficiency, not
| morpheme separation. Even in the examples above it's not
| morphemes - proper morpheme separation would be un-believ-
| ably and dost-o-pri-mech-a-tel'n-ost'.
|
| Regardless of this, Gemini is still one of the best models
| when it comes for Slavic word formation and manipulation,
| it can express novel (non-existent) words pretty well and
| doesn't seem to be confused by wrong separation. This seems
| to be the result of extensive multilingual training,
| because e.g. GPT other than the discontinued 4.5-preview
| and many Chinese models have issues with basic coherency in
| languages that heavily rely on word formation, despite
| using similar tokenizers.
| DonHopkins wrote:
| inf-ucking-credible
| redox99 wrote:
| Character level LLMs are used for detecting insults and toxic
| chat in video games and the like.
| minimaxir wrote:
| Can you give an example of a video game explicitly using
| character-level LLMs? There were prototypes of char-rnns back
| in the day for chat moderation but it has significant compute
| overhead.
| redox99 wrote:
| It's something I heard through the grapevine. But there's
| only a few big enough competitive games where toxicity is
| such a big deal, so it's not hard to guess.
|
| Character level helps with players disguising insults.
|
| Compute wise it's basically the same, but multiply token
| count by 4. Which doesn't really matter for short chat in
| video games.
| jazzyjackson wrote:
| I figure an LLM would be way better at classifying insults
| than regexing against a bad word list. Why would character
| level be desirable?
| vanviegen wrote:
| I'd imagine for simplicity - just skip the tokenizer and
| feed bytes.
| duskwuff wrote:
| Might a character-level LLM be better at recognizing poorly
| spelled (or deliberately misspelled) profanity?
| viraptor wrote:
| Yes, for small messages and relatively small scope
| dictionary, character level will work. But that's very
| different from what's tested here.
| minimaxir wrote:
| I made a response to this counterpoint in a blog post I wrote
| about a similar question posed to LLMs (how many b's are in
| blueberry): https://news.ycombinator.com/item?id=44878290
|
| > Yes, asking an LLM how many b's are in blueberry is an
| adversarial question in the sense that the questioner is
| expecting the LLM to fail. But it's not an _unfair_ question,
| and it's objectively silly to claim that LLMs such as GPT-5 can
| operate at a PhD level, but can't correctly count the number of
| letters in a word.
|
| It's a subject that the Hacker News bubble and the real world
| treat differently.
| brookst wrote:
| It's like defending a test showing hammers are terrible at
| driving screws by saying many people are unclear on how to
| use tools.
|
| It remains unsurprising that a technology that lumps
| characters together is not great at processing below its
| resolution.
|
| Now, if there are use cases other than synthetic tests where
| this capability is important, maybe there's something
| interesting. But just pointing out that one can't actually
| climb the trees pictured on the map is not that interesting.
| achierius wrote:
| And yet... now many of them can do it. I think it's
| premature to say "this technology is for X" when what it
| was _originally_ invented for was translation, and every
| capability it has developed since then has been an immense
| surprise.
| vanviegen wrote:
| > And yet... now many of them can do it.
|
| Presumably because they trained them to death on this
| useless test that people somehow just wouldn't shut up
| about.
| minimaxir wrote:
| Which is why in the linked post, I test models against
| both the "r's in strawberries" and the "b's in
| blueberries" to see if that is the case.
|
| tl;dr the first case had near perfect accuracy as
| expected for the case if the LLMs were indeed trained on
| it. The second case did not.
| viraptor wrote:
| > it's objectively silly to claim that LLMs such as GPT-5 can
| operate at a PhD level, but can't correctly count the number
| of letters in a word.
|
| I know enough PhDs with _heavy_ dyslexia that... no, there 's
| no connection here. You can be a PhD level physicist without
| being able to spell anything.
| MountDoom wrote:
| I remember people making the exact same argument about asking
| LLMs math questions back when they couldn't figure out the
| answer to 18 times 7. "They are text token predictors, they
| don't understand numbers, can we put this nonsense to rest."
|
| The whole point of LLMs is that they do more than we suspected
| they could. And there is value in making them capable of
| handling a wider selection of tasks. When an LLM started to
| count the numbers of "r"s in "strawberry", OpenAI was taking a
| victory lap.
| vanviegen wrote:
| > When an LLM started to count the numbers of "r"s in
| "strawberry", OpenAI was taking a victory lap.
|
| Were they? Or did they feel icky about spending way to much
| post-training time on such a specific and uninteresting
| skill?
| ACCount37 wrote:
| It's not as specific of a skill as you would think. Being
| both aware of tokenizer limitations and capable of working
| around them is occasionally useful for real tasks.
| _flux wrote:
| What tasks would those be, that wouldn't be better served
| by using e.g. a Python script as a tool, possibly just as
| component of the complete solution?
| ACCount37 wrote:
| Off the top of my head: the user wants LLM to help him
| solve a word puzzle. Think something a bit like Wordle,
| but less represented in its dataset.
|
| For that, the LLM needs to be able to compare words
| character by character reliably. And to do that, it needs
| at least one of: be able to fully resolve the tokens to
| characters internally within one pass, know to emit the
| candidate words in a "1 character = 1 token" fashion and
| then compare that, or know that it should defer to tool
| calls and do that.
|
| An LLM trained for better tokenization-awareness would be
| able to do that. The one that wasn't could fall into
| weird non-humanlike failures.
| _flux wrote:
| Surely there are algorithms to more effectively solve
| Wordles, and many other word puzzles, than LLMs? LLMs
| could stil be in the loop for generating words: LLM
| proposes words, deterministic algorithm tell the score
| according to the rules of the puzzle, or even augment the
| list by searching adjacent word space; then at some point
| LLM submits the guess.
|
| Given wordle words are real words, I think this kind of
| loop could fare pretty well.
| ACCount37 wrote:
| Your mistake is thinking that the user wants an algorithm
| that solves Wordles efficiently. Or that making and
| invoking a tool is always a more efficient solution.
|
| As opposed to: the user is a 9 year old girl, and she has
| this puzzle in a smartphone game, and she can't figure
| out the answer, and the mom is busy, so she asks the AI,
| because the AI is never busy.
|
| Now, for a single vaguely Wordle-like puzzle, how many
| tokens would it take to write and invoke a solver, and
| how many to just _solve it_ - working around the
| tokenizer if necessary?
|
| If you had a batch of 9000 puzzle questions, I can easily
| believe that writing and running a purpose specific
| solver would be more compute efficient. But if we're
| dealing with 1 puzzle question, and we're already
| invoking an LLM to interpret the natural language
| instructions for it? Nah.
| viraptor wrote:
| They're better at maths now, but you still shouldn't ask them
| maths questions. Same as spelling - whether they improve or
| not doesn't matter if you want a specific, precise answer -
| it's the wrong tool and the better it does, the bigger the
| trap of it failing unexpectedly.
| curioussquirrel wrote:
| Why test for something? I find it fascinating if something
| starts being good at task it is "explicitly not designed for"
| (which I don't necessarily agree with - it's more of a side
| effect of their architecture).
|
| I also don't agree that nobody is using this for - there are
| real life use cases today, such as people trying to find
| meaning of misspelled words.
|
| On a side note, I remember testing Claude 3.7 with the classic
| "R's in the word strawberry" question through their chat
| interface, and given that it's really good at tool calls, it
| actually created a website to a) count it with JavaScript, b)
| visualize it on a page. Other models I tested for the blog post
| were also giving me python code for solving the issue. This is
| definitely already a thing and it works well for some isolated
| problems.
| viraptor wrote:
| > such as people trying to find meaning of misspelled words.
|
| That worked just fine for quite a while. There's apparently
| enough misspelling in the training data, we don't need
| precise spelling for it. You can literally write drunken
| gibberish and it will work.
| curioussquirrel wrote:
| True. But does that scale to less common words? Or to other
| languages than English?
| jazzyjackson wrote:
| That's good. 1 800 chat gpt really let me down today, I like
| calling it to explain acronyms and define words since I travel
| with a flip phone without google, today I saw the word "littoral"
| and tried over and over to spell it out but the model could only
| give me the definition for "literal" (admittedly a homonym but
| hence spelling it out, Lima indigo tango tango oscar Romeo alpha
| Lima, to no avail)
|
| I said "I know you're a robot and bad at spelling but listen..."
| And got cut off with a "sorry, my guidelines won't let me help
| with that request..."
|
| Thankfully, the flip phone allows for some satisfaction when
| hanging up.
| xwolfi wrote:
| I know this word, it's French and it means coastline, coastal,
| something at the edge of the land and sea ! We use it in French
| a lot to describe positively a long coastline. I'm surprised
| it's used in an English context, but all French words can be
| used in English I guess if you're a bit "confiant" about it !
| kgwgk wrote:
| A very quick search suggests that the word entered English
| before French. (I could be wrong, I just found it
| interesting).
| tokai wrote:
| It is a latin word.
| yeasku wrote:
| Is also a Spanish word used today.
|
| There is not much latin wrote around the world nowdays.
| BoorishBears wrote:
| Did you try "literal but with an o"?
| ASalazarMX wrote:
| Even search engines have trouble with that, they assume
| you're looking for the literal (letter) named "O".
| yeasku wrote:
| The trouble:
|
| Did you meant litoral?
| ASalazarMX wrote:
| > I said "I know you're a robot and bad at spelling but
| listen..." And got cut off with a "sorry, my guidelines won't
| let me help with that request..."
|
| For some reason I like when they do that. My fondest memory was
| chatting with Copilot (when it was called Sydney), challenging
| it to a game of rock-paper-scissors, asking it to choose first,
| and winning every round to its increasing astonishment, until
| it suspected I was cheating and ended the conversation. So
| smart and so dumb.
| atleastoptimal wrote:
| I rearry rove a ripe strawberry
| NitpickLawyer wrote:
| Well, not surprising, but the latest LLMs really do get the
| gist of your joke attempt. Here's a plain, unauthenticated
| chatgpt reply:
|
| That post -- "I rearry rove a ripe strawberry" -- is a playful
| way of writing "I really love a ripe strawberry."
|
| The exaggerated misspelling ("rearrry rove") mimics the way a
| stereotyped "Engrish" or "Japanese accent" might sound when
| pronouncing English words -- replacing L sounds with R sounds.
|
| So, the user was most likely joking or being silly, trying to
| sound cute or imitate a certain meme style. However, it's worth
| noting that while this kind of humor can be lighthearted, it
| can also come across as racially insensitive, since it plays on
| stereotypes of how East Asian people speak English.
|
| In short:
|
| Literal meaning: They love ripe strawberries.
|
| Tone/intention: Playful or meme-style exaggeration.
|
| Potential issue: It relies on a racialized speech stereotype,
| so it can be offensive depending on context.
| zamalek wrote:
| It seems like they don't realize the relevance of
| "strawberry." Llms were famously incapable of counting Rs in
| strawberry not too long ago.
| zeroq wrote:
| - How many letters R are in the word `strawberry`? - There
| are seven letters R in the word `strawberry`. Would you
| like me to rearrange them?
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