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[–] 45 points 1 year ago* (64 children)

LLM wasn’t made for this

There's a thought experiment that challenges the concept of cognition, called The Chinese Room. What it essentially postulates is a conversation between two people, one of whom is speaking Chinese and getting responses in Chinese. And the first speaker wonders "Does my conversation partner really understand what I'm saying or am I just getting elaborate stock answers from a big library of pre-defined replies?"

The LLM is literally a Chinese Room. And one way we can know this is through these interactions. The machine isn't analyzing the fundamental meaning of what I'm saying, it is simply mapping the words I've input onto a big catalog of responses and giving me a standard output. In this case, the problem the machine is running into is a legacy meme about people miscounting the number of "r"s in the word Strawberry. So "2" is the stock response it knows via the meme reference, even though a much simpler and dumber machine that was designed to handle this basic input question could have come up with the answer faster and more accurately.

When you hear people complain about how the LLM "wasn't made for this", what they're really complaining about is their own shitty methodology. They build a glorified card catalog. A device that can only take inputs, feed them through a massive library of responses, and sift out the highest probability answer without actually knowing what the inputs or outputs signify cognitively.

Even if you want to argue that having a natural language search engine is useful (damn, wish we had a tool that did exactly this back in August of 1996, amirite?), the implementation of the current iteration of these tools is dogshit because the developers did a dogshit job of sanitizing and rationalizing their library of data. Also, incidentally, why Deepseek was running laps around OpenAI and Gemini as of last year.

Imagine asking a librarian "What was happening in Los Angeles in the Summer of 1989?" and that person fetching you back a stack of history textbooks, a stack of Sci-Fi screenplays, a stack of regional newspapers, and a stack of Iron-Man comic books all given equal weight? Imagine hearing the plot of the Terminator and Escape from LA intercut with local elections and the Loma Prieta earthquake.

That's modern LLMs in a nutshell.

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  • [–] 10 points 1 year ago (27 children)

    You've missed something about the Chinese Room. The solution to the Chinese Room riddle is that it is not the person in the room but rather the room itself that is communicating with you. The fact that there's a person there is irrelevant, and they could be replaced with a speaker or computer terminal.

    Put differently, it's not an indictment of LLMs that they are merely Chinese Rooms, but rather one should be impressed that the Chinese Room is so capable despite being a completely deterministic machine.

    If one day we discover that the human brain works on much simpler principles than we once thought, would that make humans any less valuable? It should be deeply troubling to us that LLMs can do so much while the mathematics behind them are so simple. Arguments that because LLMs are just scaled-up autocomplete they surely can't be very good at anything are not comforting to me at all.

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  • [–] 3 points 1 year ago (13 children)

    This. I often see people shitting on AI as "fancy autocomplete" or joking about how they get basic things incorrect like this post but completely discount how incredibly fucking capable they are in every domain that actually matters. That's what we should be worried about... what does it matter that it doesn't "work the same" if it still accomplishes the vast majority of the same things? The fact that we can get something that even approximates logic and reasoning ability from a deterministic system is terrifying on implications alone.

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  • [–] 1 point 1 year ago (12 children)

    Why doesn't the LLM know to write (and run) a program to calculate the number of characters?

    I feel like I'm missing something fundamental.

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  • [–] 2 points 1 year ago (2 children)

    You didn't get good answers so I'll explain.

    First, an LLM can easily write a program to calculate the number of rs. If you ask an LLM to do this, you will get the code back.

    But the website ChatGPT.com has no way of executing this code, even if it was generated.

    The second explanation is how LLMs work. They work on the word (technically token, but think word) level. They don't see letters. The AI behind it literally can only see words. The way it generates output is it starts typing words, and then guesses what word is most likely to come next. So it literally does not know how many rs are in strawberry. The impressive part is how good this "guessing what word comes next" is at answering more complex questions.

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  • [–] 1 point 1 year ago (1 child)

    But why can't "query the python terminal" be trained into the LLM. It just needs some UI training.

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  • [–] 1 point 1 year ago* (4 children)

    The LLM isn't aware of its own limitations in this regard. The specific problem of getting an LLM to know what characters a token comprises has not been the focus of training. It's a totally different kind of error than other hallucinations, it's almost entirely orthogonal, but other hallucinations are much more important to solve, whereas being able to count the number of letters in a word or add numbers together is not very important, since as you point out, there are already programs that can do that.

    At the moment, you can compare this perhaps to the Paris in the the Spring illusion. Why don't people know to double-check the number of 'the's in a sentence? They could just use their fingers to block out adjacent words and read each word in isolation. They must be idiots and we shouldn't trust humans in any domain.

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  • [–] 2 points 1 year ago (3 children)

    The most convincing arguments that llms are like humans aren't that llm's are good, but that humans are just unrefrigerated meat and personhood is a delusion.

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  • [–] 1 point 1 year ago (2 children)

    This might well be true yeah. But that's still good news for AI companies who want to replace humans -- bar's lower than they thought.

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  • [–] 1 point 1 year ago* (3 children)

    It doesn't know things.

    It's a statistical model. It cannot synthesize information or problem solve, only show you a rough average of it's library of inputs graphed by proximity to your input.

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  • [–] 1 point 1 year ago (2 children)

    Congrats, you've discovered reductionism. The human brain also doesn't know things, as it's composed of electrical synapses made of molecules that obey the laws of physics and direct one's mouth to make words in response to signals that come from the ears.

    Not saying LLMs don't know things, but your argument as to why they don't know things has no merit.

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  • [–] 1 point 1 year ago (6 children)

    one should be impressed that the Chinese Room is so capable despite being a completely deterministic machine.

    I'd be more impressed if the room could tell me how many "r"s are in Strawberry inside five minutes.

    If one day we discover that the human brain works on much simpler principles

    Human biology, famous for being simple and straightforward.

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  • [–] 1 point 1 year ago* (4 children)

    Because LLMs operate at the token level, I think it would be a more fair comparison with humans to ask why humans can't produce the IPA spelling words they can say, /nɔr kæn ðeɪ ˈizəli rid θɪŋz ˈrɪtən ˈpjʊrli ɪn aɪ pi ˈeɪ/ despite the fact that it should be simple to -- they understand the sounds after all. I'd be impressed if somebody could do this too! But that most people can't shouldn't really move you to think humans must be fundamentally stupid because of this one curious artifact. Maybe they are fundamentall stupid for other reasons, but this one thing is quite unrelated.

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  • [–] 1 point 1 year ago (3 children)

    why humans can’t produce the IPA spelling words they can say, /nɔr kæn ðeɪ ˈizəli rid θɪŋz ˈrɪtən ˈpjʊrli ɪn aɪ pi ˈeɪ/ despite the fact that it should be simple to – they understand the sounds after all

    That's just access to the right keyboard interface. Humans can and do produce those spellings with additional effort or advanced tool sets.

    humans must be fundamentally stupid because of this one curious artifact.

    Humans turns oatmeal into essays via a curios lump of muscle is an impressive enough trick on its face.

    LLMs have 95% of the work of human intelligence handled for them and still stumble on the last bits.

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  • [–] 1 point 1 year ago* (2 children)

    I mean, among people who are proficient with IPA, they still struggle to read whole sentences written entirely in IPA. Similarly, people who speak and read chinese struggle to read entire sentences written in pinyin. I'm not saying people can't do it, just that it's much less natural for us (even though it doesn't really seem like it ought to be.)

    I agree that LLMs are not as bright as they look, but my point here is that this particular thing -- their strange inconsistency understanding what letters correspond to the tokens they produce -- specifically shouldn't be taken as evidence for or against LLMs being capable in any other context.

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  • [–] 7 points 1 year ago (5 children)

    You might just love Blind Sight. Here, they're trying to decide if an alien life form is sentient or a Chinese Room:

    "Tell me more about your cousins," Rorschach sent.

    "Our cousins lie about the family tree," Sascha replied, "with nieces and nephews and Neandertals. We do not like annoying cousins."

    "We'd like to know about this tree."

    Sascha muted the channel and gave us a look that said Could it be any more obvious? "It couldn't have parsed that. There were three linguistic ambiguities in there. It just ignored them."

    "Well, it asked for clarification," Bates pointed out.

    "It asked a follow-up question. Different thing entirely."

    Bates was still out of the loop. Szpindel was starting to get it, though.. .

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  • [–] 6 points 1 year ago* (4 children)

    Blindsight is such a great novel. It has not one, not two but three great sci-fi concepts rolled into one book.

    One is artificial intelligence (the ship's captain is an AI), the second is alien life so vastly different it appears incomprehensible to human minds. And last but not least, and the most wild, vampires as a evolutionary branch of humanity that died out and has been recreated in the future.

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  • [–] 4 points 1 year ago

    My a favorite part of the vampire thing is how they died out. Turns out vampires start seizing when trying to visually process 90° angles, and humans love building shit like that (not to mention a cross is littered with them). It's so mundane an extinction I'd almost believe it.

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  • [–] 4 points 1 year ago* (last edited 1 year ago) (2 children)

    Also, the extremely post-cyberpunk posthumans, and each member of the crew is a different extremely capable kind of fucked up model of what we might become, with the protagonist personifying the genre of horror that it is, while still being occasionally hilarious.

    Despite being fundamentally a cosmic horror novel, and relentlessly math-in-the-back-of-the-book hard scifi it does what all the best cyberpunk does and shamelessly flirts with the supernatural at every opportunity. The sequel doubles down on this, and while not quite as good overall (still exceptionally good, but harder to follow) each of the characters explores a novel and sweet+sad+horrifying kind of love.

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  • [–] 1 point 1 year ago (1 child)

    Oooh, I didn't even know it had a sequel!

    I wouldn't say it flirts with the supernatural as much as it's with one foot into weird fiction, which is where cosmic horror comes from.

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  • Characters in the sequel include a hive-mind of post-science innovation monks, a straight up witch who charges their monastery at the head of a zombie army, and a plotline about finding what the monks think might be god. And that first scene, which is absolute fire btw.

    Primary themes include... Well the bit of exposition about needing to 'crawl off one mountain and cross a valley to reach higher peaks of understanding', and coping as a mostly baseline human surrounded by superintelligences, 'sufficiently advanced technology', etc.

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  • That's a very long answer to my snarky little comment :) I appreciate it though. Personally, I find LLMs interesting and I've spent quite a while playing with them. But after all they are like you described, an interconnected catalogue of random stuff, with some hallucinations to fill the gaps. They are NOT a reliable source of information or general knowledge or even safe to use as an "assistant". The marketing of LLMs as being fit for such purposes is the problem. Humans tend to turn off their brains and to blindly trust technology, and the tech companies are encouraging them to do so by making false promises.

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  • [–] 3 points 1 year ago (10 children)

    a much simpler and dumber machine that was designed to handle this basic input question could have come up with the answer faster and more accurately

    The human approach could be to write a (python) program to count the number of characters precisely.

    When people refer to agents, is this what they are supposed to be doing? Is it done in a generic fashion or will it fall over with complexity?

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  • [–] 2 points 1 year ago* (last edited 1 year ago) (2 children)

    No, this isn't what 'agents' do, 'agents' just interact with other programs. So like move your mouse around to buy stuff, using the same methods as everything else.

    Its like a fancy diversely useful diversely catastrophic hallucination prone API.

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  • [–] 1 point 1 year ago (1 child)

    'agents' just interact with other programs.

    If that other program is, say, a python terminal then can't LLMs be trained to use agents to solve problems outside their area of expertise?

    I just tested chatgpt to write a python program to return the frequency of letters in a string, then asked it for the number of L's in the longest placename in Europe.

    ''''

    String to analyze

    text = "Llanfairpwllgwyngyllgogerychwyrndrobwllllantysiliogogogoch"

    Convert to lowercase to count both 'L' and 'l' as the same

    text = text.lower()

    Dictionary to store character frequencies

    frequency = {}

    Count characters

    for char in text: if char in frequency: frequency[char] += 1 else: frequency[char] = 1

    Show the number of 'l's

    print("Number of 'l's:", frequency.get('l', 0))

    '''

    I was impressed until

    Output

    Number of 'l's: 16

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  • [–] 1 point 1 year ago (12 children)

    Imagine asking a librarian "What was happening in Los Angeles in the Summer of 1989?" and that person fetching you ... That's modern LLMs in a nutshell.

    I agree, but I think you're still being too generous to LLMs. A librarian who fetched all those things would at least understand the question. An LLM is just trying to generate words that might logically follow the words you used.

    IMO, one of the key ideas with the Chinese Room is that there's an assumption that the computer / book in the Chinese Room experiment has infinite capacity in some way. So, no matter what symbols are passed to it, it can come up with an appropriate response. But, obviously, while LLMs are incredibly huge, they can never be infinite. As a result, they can often be "fooled" when they're given input that semantically similar to a meme, joke or logic puzzle. The vast majority of the training data that matches the input is the meme, or joke, or logic puzzle. LLMs can't reason so they can't distinguish between "this is just a rephrasing of that meme" and "this is similar to that meme but distinct in an important way".

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