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On the contrary, anthropomorphism IMO is the main problem with narratives around LLMs - people are genuinely talking about them thinking and reasoning when they

• cmenge 1y ago
  I kinda agree with both of you. It might be a required abstraction, but it's a leaky one. Long before LLMs, I would talk about classes / functions / modules like "it then does this, decides the epsilon is too low, chops it up and adds it to the list". The difference I guess it was only to a technical crowd and nobody would mistake this for anything it wasn't. Everybody know that "it" didn't "decide" anything. With AI being so mainstream and the math being much more elusive than a simple if..then I guess it's just too easy to take this simple speaking convention at face value. EDIT: some clarifications / wording
  • flir 1y ago
    Agreeing with you, this is a "can a submarine swim" problem IMO. We need a new word for what LLMs are doing. Calling it "thinking" is stretching the word to breaking point, but "selecting the next word based on a complex statistical model" doesn't begin to capture what they're capable of. Maybe it's cog-nition (emphasis on the cog).
    • whilenot-dev 1y ago
      "predirence" -> prediction meets inference and it sounds a bit like preference
      • psychoslave 1y ago
        Except -ence is a regular morph, and you would rather suffix it to predict(at)-. And prediction is already an hyponym of inference. Why not just use inference then?
        • whilenot-dev 1y ago
          I didn't think of prediction in the statistical sense here, but rather as a prophecy based on a vision, something that is inherently stored in a model without the knowledge of the modelers. I don't want to imply any magic or something supernatural here, it's just the juice that goes off the rails sometimes, and it gets overlooked due to the sheer quantity of the weights. Something like unknown bugs in production, but, because they still just represent a valid number in some computation that wouldn't cause any panic, these few bits can show a useful pattern under the right circumstances. Inference would be the part that is deliberately learned and drawn from conclusions based on the training set, like in the "classic" sense of statistical learning.
    • LeonardoTolstoy 1y ago
      What does a submarine do? Submarine? I suppose you "drive" a submarine which is getting to the idea: submarines don't swim because ultimately they are "driven"? I guess the issue is we don't make up a new word for what submarines do, we just don't use human words. I think the above poster gets a little distracted by suggesting the models are creative which itself is disputed. Perhaps a better term, like above, would be to just use "model". They are models after all. We don't make up a new portmanteau for submarines. They float, or drive, or submarine around. So maybe an LLM doesn't "write" a poem, but instead "models a poem" which maybe indeed take away a little of the sketchy magic and fake humanness they tend to be imbued with.
      • FeepingCreature 1y ago
        Humans certainly model inputs. This is just using an awkward word and then making a point that it feels awkward.
      • flir 1y ago
        I really like that, I think it has the right amount of distance. They don't write, they model writing. We're very used to "all models are wrong, some are useful", "the map is not the territory", etc.
        • galangalalgol 1y ago
          No one was as bothered when we anthropomorphized crud apps simply for the purpose of conversing about "them". "Ack! The thing is corrupting tables again because it thinks we are still using api v3! Who approved that last MR?!" The fact that people are bothered by the same language now is indicative in itself. If you want to maintain distance, pre prompt models to structure all conversations to lack pronouns as between a non sentient language model and a non sentient agi. You can have the model call you out for referring to the model as existing. The language style that forces is interesting, and potentially more productive except that there are fewer conversations formed like that in the training dataset. Translation being a core function of language models makes it less important thought. As for confusing the map for the territory, that is precisely what philosophers like Metzinger say humans are doing by considering "self" to be a real thing and that they are conscious when they are just using the reasoning shortcut of narrating the meta model to be the model.
    • psychoslave 1y ago
      It does some kind of automatic inference (AI), and that's it.
    • JimDabell 1y ago
      > this is a "can a submarine swim" problem IMO. We need a new word for what LLMs are doing. Why? A plane is not a fly and does not stay aloft like a fly, yet we describe what it does as flying despite the fact that it does not flap its wings. What are the downsides we encounter that are caused by using the word “fly” to describe a plane travelling through the air?
      • flir 1y ago
        I was riffing on that famous Dijkstra quote.
      • dotancohen 1y ago
        For what it's worth, in my language the motion of birds and the motion of aircraft _are_ two different words.
      • Tijdreiziger 1y ago
        Flying isn’t named after flies, they both come from the same root. https://www.etymonline.com/search?q=fly https://www.etymonline.com/search?q=fly
      • lelanthran 1y ago
        > A plane is not a fly and does not stay aloft like a fly, yet we describe what it does as flying despite the fact that it does not flap its wings. Flying doesn't mean flapping, and the word has a long history of being used to describe inanimate objects moving through the air. "A rock flies through the window, shattering it and spilling shards everywhere" - see? OTOH, we have never used to word "swim" in the same way - "The rock hit the surface and swam to the bottom" is wrong!
    • intended 1y ago
      It will help significantly, to realize that the only thinking happening is when the human looks at the output and attempts to verify if it is congruent with reality. The rest of the time it’s generating content.
    • Atlas667 1y ago
      A machine that can imitate the products of thought is not the same as thinking. All imitations require analogous mechanisms, but that is the extent of their similarities, in syntax. Thinking requires networks of billions of neurons, and then, not only that, but words can never exist on a plane because they do not belong to a plane. Words can only be stored on a plane, they are not useful on a plane. Because of this LLMs have the potential to discover new aspects and implications of language that will be rarely useful to us because language is not useful within a computer, it is useful in the world. Its like seeing loosely related patterns in a picture and keep derivating on those patterns that are real, but loosely related. LLMs are not intelligence but its fine that we use that word to describe them.
    • delusional 1y ago
      > "selecting the next word based on a complex statistical model" doesn't begin to capture what they're capable of. I personally find that description perfect. If you want it shorter you could say that an LLM generates.
    • ryeats 1y ago
      It's more like muscle memory than cognition. So maybe procedural memory but that isn't catchy.
      • 01HNNWZ0MV43FF 1y ago
        They certainly do act like a thing which has a very strong "System 1" but no "System 2" (per Thinking, Fast And Slow)
    • seanhunter 1y ago
      This is a total non-problem that has been invented by people so they have something new and exciting to be pedantic about. When we need to speak precisely about a model and how it works, we have a formal language (mathematics) which allows us to be absolutely specific. When we need to empirically observe how the model behaves, we have a completely precise method of doing this (running an eval). Any other time, we use language in a purposefully intuitive and imprecise way, and that is a deliberate tradeoff which sacrifices precision for expressiveness.
  • loxs 1y ago
    We can argue all day what "think" means and whether a LLM thinks (probably not IMO), but at least in my head the threshold for "decide" is much lower so I can perfectly accept that a LLM (or even a class) "decides". I don't have a conflict about that. Yeah, it might not be a decision in the human sense, but it's a decision in the mathematical sense so I have always meant "decide" literally when I was talking about a piece of code. It's much more interesting when we are talking about... say... an ant... Does it "decide"? That I have no idea as it's probably somewhere in between, neither a sentient decision, nor a mathematical one.
    • 0x457 1y ago
      Well, it outputs a chain of thoughts that later used to produce better prediction. It produces a chain of thoughts similar to how one would do thinking about a problem out loud. It's more verbose that what you would do, but you always have some ambient context that LLM lacks.
  • stoneyhrm1 1y ago
    I mean you can boil anything down to it's building blocks and make it seem like it didn't 'decide' anything. When you as a human decide something, your brain and it's neurons just made some connections with an output signal sent to other parts that resulting in your body 'doing' something. I don't think LLMs are sentient or any bullshit like that, but I do think people are too quick to write them off before really thinking about how a nn 'knows things' similar to how a human 'knows' things, it is trained and reacts to inputs and outputs. The body is just far more complex.
    • grey-area 1y ago
      I wasn't talking about knowing (they clearly encode knowledge), I was talking about thinking/reasoning, which is something LLMs do not in fact do IMO. These are very different and knowledge is not intelligence.
      • chpatrick 1y ago
        To me all of those are so vaguely defined that arguing whether an LLM is "really really" doing something is kind of a waste of time. It's like we're clinging on to things that make us feel like human cognition is special so we're saying LLM's arent "really" doing it, then not defining what it actually is.
  • HelloUsername 1y ago
    > EDIT: some clarifications / wording This made me think, when will we see LLMs do the same; rereading what they just sent, and editing and correcting their output again :P
• Al-Khwarizmi 1y ago
  I think it's worth distinguishing between the use of anthropomorphism as a useful abstraction and the misuse by companies to fuel AI hype. For example, I think "chain of thought" is a good name for what it denotes. It makes the concept easy to understand and discuss, and a non-antropomorphized name would be unnatural and unnecessarily complicate things. This doesn't mean that I support companies insisting that LLMs think just like humans or anything like that. By the way, I would say actually anti-anthropomorphism has been a bigger problem for understanding LLMs than anthropomorphism itself. The main proponents of anti-anthropomorphism (e.g. Bender and the rest of "stochastic parrot" and related paper authors) came up with a lot of predictions about things that LLMs surely couldn't do (on account of just being predictors of the next word, etc.) which turned out to be spectacularly wrong.
  • whilenot-dev 1y ago
    I don't know about others, but I much prefer if some reductionist tries to conclude what's technically feasible and is proven wrong over time, than somebody yelling holistic analogies á la "it's sentient, it's intelligent, it thinks like us humans" for the sole dogmatic reason of being a futurist. Tbh I also think your comparison that puts "UI events -> Bits -> Transistor Voltages" as analogy to "AI thinks -> token de-/encoding + MatMul" is certainly a stretch, as the part about "Bits -> Transistor Voltages" applies to both hierarchies as the foundational layer. "chain of thought" could probably be called "progressive on-track-inference" and nobody would roll an eye.
• amelius 1y ago
  I don't agree. Most LLMs have been trained on human data, so it is best to talk about these models in a human way.
  • 4ndrewl 1y ago
    Even the verb 'trained' is contentious wrt anthropomorphism.
    • amelius 1y ago
      Somewhat true but rodents can also be trained ...
      • 4ndrewl 1y ago
        Rodents aren't functions though?
        • FeepingCreature 1y ago
          Every computable system, even stateful systems, can be reformulated as a function. If IO can be functional, I don't see why mice can't.
          • psychoslave 1y ago
            Well, that's a strong claim of equivalence between computationable models and realty. The consensual view is rather that no map is matching fully the territory, or said otherwise the territory includes ontological components that exceeds even the most sophisticated map that can be ever built.
            • FeepingCreature 1y ago
              I believe the consensus view is that physics is computable.
• fenomas 1y ago
  When I see these debates it's always the other way around - one person speaks colloquially about an LLM's behavior, and then somebody else jumps on them for supposedly believing the model is conscious, just because the speaker said "the model thinks.." or "the model knows.." or whatever. To be honest the impression I've gotten is that some people are just very interested in talking about not anthropomorphizing AI, and less interested in talking about AI behaviors, so they see conversations about the latter as a chance to talk about the former.
  • latexr 1y ago
    Respectfully, that is a reflection of the places you hang out in (like HN) and not the reality of the population. Outside the technical world it gets much worse. There are people who killed themselves because of LLMs, people who are in love with them, people who genuinely believe they have “awakened” their own private ChatGPT instance into AGI and are eschewing the real humans in their lives.
    • fenomas 1y ago
      Naturally I'm aware of those things, but I don't think TFA or GGP were commenting on them so I wasn't either.
    • Xss3 1y ago
      The other day a good friend of mine with mental health issues remarked that "his" chatgpt understands him better than most of his friends and gives him better advice than his therapist. It's going to take a lot to get him out of that mindset and frankly I'm dreading trying to compare and contrast imperfect human behaviour and friendships with a sycophantic AI.
      • bonoboTP 1y ago
        It's surprisingly common on reddit that people talk about "my chatgpt", and they don't always seem like the type who are "in a relationship" with the bot or unlocking the secrets of the cosmos with it, but still they write "my chatgpt" and "your chatgpt". I guess the custom prompt and the available context does customize the model for them in some sense, but I suspect they likely have a wrong mental model of how this customization works. I guess they imagine it as their own little model being stored on file at OpenAI and as they interact with it, it's being shaped by it, and each time they connect, their model is retrieved from the cloud storage and they connect to it or something.
• stoneyhrm1 1y ago
  I thought this too but then began to think about it from the perspective of the programmers trying to make it imitate human learning. That's what a nn is trying to do at the end of the day, and in the same way I train myself by reading problems and solutions, or learning vocab at a young age, it does so by tuning billions of parameters. I think these models do learn similarly. What does it even mean to reason? Your brain knows certain things so it comes to certain conclusions, but it only knows those things because it was ''trained'' on those things. I reason my car will crash if I go 120 mph on the other side of the road because previously I have 'seen' where the input is a car going 120mph has a high probability of producing a crash, and similarly have seen input where the car is going on the other side of the road, producing a crash. Combining the two would tell me it's a high probability.
• losvedir 1y ago
  Well "reasoning" refers to Chain-of-Thought and if you look at the generated prompts it's not hard to see why it's called that. That said, it's fascinating to me that it works (and empirically, it does work; a reasoning model generating tens of thousands of tokens while working out the problem does produce better results). I wish I knew why. A priori I wouldn't have expected it, since there's no new input. That means it's all "in there" in the weights already. I don't see why it couldn't just one shot it without all the reasoning. And maybe the future will bring us more distilled models that can do that, or they can tease out all that reasoning with more generated training data, to move it from dispersed around the weights -> prompt -> more immediately accessible in the weights. But for now "reasoning" works. But then, at the back of my mind is the easy answer: maybe you can't optimize it. Maybe the model has to "reason" to "organize its thoughts" and get the best results. After all, if you give me a complicated problem I'll write down hypotheses and outline approaches and double check results for consistency and all that. But now we're getting dangerously close to the "anthropomorphization" that this article is lamenting.
  • sdenton4 1y ago
    CoT gives the model more time to think and process the inputs it has. To give an extreme example, suppose you are using next token prediction to answer 'Is P==NP?' The tiny number of input tokens means that there's a tiny amount of compute to dedicate to producing an answer. A scratchpad allows us to break free of the short-inputs problem. Meanwhile, things can happen in the latent representation which aren't reflected in the intermediate outputs. You could, instead of using CoT, say "Write a recipe for a vegetarian chile, along with a lengthy biographical story relating to the recipe. Afterwards, I will ask you again about my original question." And the latents can still help model the primary problem, yielding a better answer than you would have gotten with the short input alone. Along these lines, I believe there are chain of thought studies which find that the content of the intermediate outputs don't actually matter all that much...
  • shakadak 1y ago
    > I don't see why it couldn't just one shot it without all the reasoning. That's reminding me of deep neural networks where single layer networks could achieve the same results, but the layer would have to be excessively large. Maybe we're re-using the same kind of improvement, scaling in length instead of width because of our computation limitations ?
• bunderbunder 1y ago
  "All models are wrong, but some models are useful," is the principle I have been using to decide when to go with an anthropomorphic explanation. In other words, no, they never accurately describe what the LLM is actually doing. But sometimes drawing an analogy to human behavior is the most effective way to pump others' intuition about a particular LLM behavior. The trick is making sure that your audience understands that this is just an analogy, and that it has its limitations. And it's not completely wrong. Mimicking human behavior is exactly what they're designed to do. You just need to keep reminding people that it's only doing so in a very superficial and spotty way. There's absolutely no basis for assuming that what's happening on the inside is the same.
  • Veen 1y ago
    Some models are useful in some contexts but wrong enough to be harmful in others.
    • bunderbunder 1y ago
      All models are useful in some contexts but wrong enough to be harmful in others. Relatedly, the alternative to pragmatism is analysis paralysis.
• bakuninsbart 1y ago
  > people are genuinely talking about them thinking and reasoning when they are doing nothing of that sort With such strong wording, it should be rather easy to explain how our thinking differs from what LLMs do. The next step - showing that what LLMs do precludes any kind of sentience is probably much harder.
• ordu 1y ago
  > On the contrary, anthropomorphism IMO is the main problem with narratives around LLMs I hold a deep belief that anthropomorphism is a way the human mind words. If we take for granted the hypothesis of Franz de Waal, that human mind developed its capabilities due to political games, and then think about how it could later lead to solving engineering and technological problems, then the tendency of people to anthropomorphize becomes obvious. Political games need empathy or maybe some other kind of -pathy, that allows politicians to guess motives of others looking at their behaviors. Political games directed the evolution to develop mental instruments to uncover causality by watching at others and interacting with them. Now, to apply these instruments to inanimate world all you need is to anthropomorphize inanimate objects. Of course, it leads sometimes to the invention of gods, or spirits, or other imaginary intelligences behinds things. And sometimes these entities get in the way of revealing the real causes of events. But I believe that to anthropomorphize LLMs (at the current stage of their development) is not just the natural thing for people but a good thing as well. Some behavior of LLMs is easily described in terms of psychology; some cannot be described or at least not so easy. People are seeking ways to do it. Projecting this process into the future, I can imagine how there will be a kind of consensual LLMs "theory" that explains some traits of LLMs in terms of human psychology and fails to explain other traits, so they are explained in some other terms... And then a revolution happens, when a few bright minds come and say that "anthropomorphism is bad, it cannot explain LLM" and they propose something different. I'm sure it will happen at some point in the future, but not right now. And it will happen not like that: not just because someone said that anthropomorphism is bad, but because they proposed another way to talk about reasons behind LLMs behavior. It is like with scientific theories: they do not fail because they become obviously wrong, but because other, better theories replace them. It doesn't mean, that there is no point to fight anthropomorphism right now, but this fight should be directed at searching for new ways to talk about LLMs, not to show at the deficiencies of anthropomorphism. To my mind it makes sense to start not with deficiencies of anthropomorphism but with its successes. What traits of LLMs it allows us to capture, which ideas about LLMs are impossible to wrap into words without thinking of LLMs as of people?
• marviel 1y ago
  how do you account for the success of reasoning models? I agree these things don't think like we do, and that they have weird gaps, but to claim they can't reason at all doesn't feel grounded.
• godelski 1y ago
  Serendipitous name... In part I agree with the parent. >> it pointless to *not* anthropomorphize, at least to an extent. I agree that it is pointless to not anthropomorphize because we are humans and we will automatically do this. Willingly or unwillingly. On the other hand, it generates bias. This bias can lead to errors. So the real answer is (imo) that it is fine to anthropomorphise but recognize that while doing so can provide utility and help us understand, it is WRONG. Recognizing that it is not right and cannot be right provides us with a constant reminder to reevaluate. Use it, but double check, and keep checking making sure you understand the limitations of the analogy. Understanding when and where it applies, where it doesn't, and most importantly, where you don't know if it does or does not. The last is most important because it helps us form hypotheses that are likely to be testable (likely, not always. Also, much easier said than done). So I pick a "grey area". Anthropomorphization is a tool that can be helpful. But like any tool, it isn't universal. There is no "one-size-fits-all" tool. Literally, one of the most important things for any scientist is to become an expert at the tools you use. It's one of the most critical skills of *any expert*. So while I agree with you that we should be careful of anthropomorphization, I disagree that it is useless and can never provide information. But I do agree that quite frequently, the wrong tool is used for the right job. Sometimes, hacking it just isn't good enough.
• UncleOxidant 1y ago
  It's not just distorting discussions it's leading people to put a lot of faith in what LLMs are telling them. Was just on a zoom an hour ago where a guy working on a startup asked ChatGPT about his idea and then emailed us the result for discussion in the meeting. ChatGPT basically just told him what he wanted to hear - essentially that his idea was great and it would be successful ("if you implement it correctly" was doing a lot of work). It was a glowing endorsement of the idea that made the guy think that he must have a million dollar idea. I had to be "that guy" who said that maybe ChatGPT was telling him what he wanted to hear based on the way the question was formulated - tried to be very diplomatic about it and maybe I was a bit too diplomatic because it didn't shake his faith in what ChatGPT had told him.
  • TimTheTinker 1y ago
    LLMs directly exploit a human trust vuln. Our brains tend to engage with them relationally and create an unconscious functional belief that an agent on the other end is responding with their real thoughts, even when we know better. AI apps ought to at minimum warn us that their responses are not anyone's (or anything's) real thoughts. But the illusion is so powerful that many people would ignore the warning.
• nomel 1y ago
  > people are genuinely talking about them thinking and reasoning when they are doing nothing of that sort Do you believe thinking/reasoning is a binary concept? If not, do you think the current top LLM are before or after the 50% mark? What % do you think they're at? What % range do you think humans exhibit?