[HN Gopher] Talking About Large Language Models ___________________________________________________________________ Talking About Large Language Models Author : negativelambda Score : 83 points Date : 2022-12-10 16:12 UTC (6 hours ago) (HTM) web link (arxiv.org) (TXT) w3m dump (arxiv.org) | gamegoblin wrote: | Everyone pointing out how LLMs fail at some relatively simple | tasks are fundamentally misunderstanding the utility of LLMs. | | Don't think of an LLM as a full "computer" or "brain". Think of | it like a CPU. Your CPU can't run whole programs, it runs single | instructions. The rest of the computer built around the CPU gives | it the ability to run programs. | | Think of the LLM like a neural CPU whose instructions are | relatively simple English commands. Wrap the LLM in a script that | executes commands in a recursive fashion. | | Yes, you can get the LLM to do complicated things in a single | pass, this is a testament to the sheer size and massive training | set of GPT3 and its ilk. But even with GPT3 you will have more | success with wrapper programs structured like: | premise = gpt3("write an award winning movie premise) | loop 5 times: critique = gpt3("write a critique of | the premise", premise) premise = gpt3("rewrite the | premise taking into account the critique", premise, critique) | print(premise) | | This program breaks down the task of writing a good premise into | a cycle of writing/critique/rewriting. You will get better | premises this way than if you just expect the model to output one | on the first go. | | You can somewhat emulate a few layers of this without wrapper | code by giving it a sequence of commands, like "Write a movie | premise, then write a critique of the movie premise, then rewrite | the premise taking into account the critique". | | The model is just trained to take in some text and predict the | next word (token, really, but same idea). Its training data is a | copy of a large swath of the internet. When humans write, they | have the advantage of thinking in a recursive fashion offline, | then writing. They often edit and rewrite before posting. GPT's | training process can't see any of this out-of-text process. | | This is why it's not great at logical reasoning problems without | careful prompting. Humans tend to write text in the format | "<thesis/conclusion statement><supporting arguments>". So GPT, | being trained on human writing, is trained to emit a conclusion | _first_. But humans don 't _think_ this way, they just _write_ | this way. But GPT doesn 't have the advantage of offline | thinking. So it often will state bullshit conclusions first, and | then conjure up supporting arguments for it. | | GPT's output is like if you ask a human to start writing without | the ability to press the backspace key. It doesn't even have a | cognitive idea that such a process exists due to its architecture | and training. | | To extract best results, you have to bolt on this "recursive | thinking process" manually. For simple problems, you can do this | without a wrapper script with just careful prompting. I.e. for | math/logic problems, tell it solve the problem and show its work | along the way. It will do better since this forces it to "think | through" the problem rather than just stating a conclusion first. | lachlan_gray wrote: | This makes me wonder if GPT could be any good at defining its | own control flow. E.g. asking it to to write a python script | that uses control structures along with calls to GPT to | synthesize coherent content. Maybe it could give itself a kind | of working memory. | gamegoblin wrote: | Libraries such as https://github.com/hwchase17/langchain | allow for easy programmatic pipelines of GPT "programs". So | you could imagine taking a few hundred of these programs | written by humans for various tasks, as are sure to come into | existence in the next year or two, then adding those programs | to the training data and training a new GPT that knows how to | write programs that call itself. | lachlan_gray wrote: | Wow. Thank you for sharing. I had no idea there was a scene | for this. | sphinxster wrote: | Thank you for this interesting insight I haven't seen before. | | Are there any datasets out there that provide the full edit | stream of a human from idea to final refinement, that a model | could be trained on? | gamegoblin wrote: | REPL transcripts (i.e. bash sessions, python REPL, etc) tend | to be pretty good demonstrations of "working up to a | conclusion". And, not coincidentally, putting GPT in a REPL | environment yields better results. | | Other good examples narratives that include a lot of internal | monologue. Thing a book written in the form: | | > The sphinx asked him, "A ham sandwich costs $1.10. The ham | costs $1 more than the bread. How much does the bread cost?" | | > He thought carefully. He knew the sphinx asked tricky | problems. If the ham costs a dollar more than the bread, the | bread couldn't possibly be more than 10 cents. But if the | bread was 10 cents, the ham would be $1.10 and the total | would be $1.20. That can't be. We need to lose 10 cents, and | it has to be divided evenly among the ham and bread to | maintain the dollar offset. So the ham must be $1.05 and the | bread must be $0.05. He answered the sphinx confidentally | "The bread is $0.05!". | btbuildem wrote: | Very well put! Having played with it for a week straight, I've | come to a similar observation -- it's a generator engine, with | a "soft" interface. You still have to have skill and | understanding to use it effectively, but it's a great force | multiplier, because it removes the friction around the initial | interactions. | | If you're solving a complex problem, you cannot expect it to | "reason" about it. You have to break the problem into simpler | pieces, then you can have the LLM do the grunt work for each | piece. | albystein wrote: | This a very well put comment with a great analogy. A new | emerging paradigm of action-driven LLMs is taking the approach | of using the reasoning abilities of LLMs to drive agents that | can take actions, interact with other tools and computer | programs, and perform useful tasks like autonomously | programming, customer support, etc | | And I think you're right when you say that they're lacking in | recursive thinking abilities. However, their reasoning | abilities are pretty excellent which is why when you prompt | them to think step-by-step, or break down problems to them, | they correctly output the right answer. | gillesjacobs wrote: | I am NLP researcher who volunteers for peer review often and the | anthropomorphisms in papers are indeed very common and very | wrong. I have to ask authors to not ascribe cognition to their | deep learning approaches in about a third of the papers I review. | | People do this because mirroring cognition to machine learning | lends credence that their specific modeling mechanism mimicks | human understanding and so is closer "to the real thing". | Obviously this is almost never the case, unless they explicitly | use biomimetic methods in which case they are often outperformed | by non-biomimetic state-of-the-art approaches. | | Thanks OP for giving me citation ammo to refer to in my | obligatory "don't humanise AI" section of reviews. (It is so | common I copy paste this section from a template). | fourfivefour wrote: | bias infests research as well as seen by the replication | crisis. So you being a researcher doesn't give more credence to | your words especially given that the state of current research | cannot fully comprehend what these ML models are doing | internally. | | I do agree that we can't ascribe cognition to machine learning. | | But I also believe that we can't ascribe that it's NOT | cognition. Why? Because we don't even truly understand what | "Knowing" or cognition is. We can't even ascribe a quantitative | similarity metric. | | What we are seeing is that those inputs and outputs look | remarkably similar to the real thing. How similar it is | internally is not a known thing. | | That's why even though you're an NLP researcher, I still say | your argument here is just as niave as the person who claims | these things are sentient. You simply don't know. No one does. | gillesjacobs wrote: | In science, if you don't know, you don't make the claim, that | is basic positivism and the scientific method. | | So basic in fact, I was thought this in elementary school. So | far ad-hominem attributions of naivety. | | Anyone that humanises computation is not only committing an | A.I. faux-pas but are going against the basic scientific | method. | oneoneonetwo wrote: | > In science, if you don't know, you don't make the claim, | that is basic positivism and the scientific method. | | Yes you're correct. So you can't make the claim that it's | NOT cognition. That is my point. You also can't make the | claim that it is cognition which was the OTHER point. | Completely agree with your statement here. | | But it goes further then this, and your statement shows YOU | don't understand science. | | >So basic in fact, I was thought this in elementary school. | So far ad-hominem attributions of naivety. | | No science is complex and basically most people don't | understand the scientific method and it's limitations. It's | not basic at all, not even people who graduate from four | year colleges in STEM fully understand the true nature of | science. Or even many scientists! | | In science and therefore reality as we know it; nothing can | be proven. This is because every subsequent observation can | completely contradict an initial claim. Proof is the domain | of logic and math, it doesn't exist in reality. Things can | be disproven but nothing can actually be proven. That is | science. | | This is subtle stuff, but it's legit. I'll quote Einstein | if you don't believe me: | | "No amount of experimentation can ever prove me right; a | single experiment can prove me wrong." - Einstein | | And a link for further investigation: | https://en.wikipedia.org/wiki/Falsifiability | | Anyway all of this says that NO claim can be made about | anything unless it's disproof. Which is exactly inline with | what I'm saying. | | Still claims are made all the time anyway in academia and | the majority of these claims aren't technically scientific. | This occurs because we can't practically operate on | anything in reality if we can't in actuality claim things | are true. So we do it anyway despite lack of any form of | actual proof. | | >Anyone that humanises computation is not only committing | an A.I. faux-pas but are going against the basic scientific | method. | | But so is dismissing any similarity to humans. You can't | technically say it's wrong or right. Especially when the | outputs and inputs to these models are very similar to what | humans would say. | | This is basic preschool stuff I knew this when I was a | baby! I thought everybody knew this! <Joking>. | gillesjacobs wrote: | It is entirely valid to demand SCIENTIFIC PAPERS adhere | to the SCIENTIFIC METHOD (exception for some domains of | the Humanities). If you do not recognize that, then we | will have to agree to disagree. | oneoneonetwo wrote: | You didn't read my comment. | | I agree with you scientific papers MUST ADHERE to the | scientific method. My comment wasn't even about that. | | My comment was about how YOU don't UNDERSTAND what | SCIENCE IS. | | Even as a researcher, many don't understand science. My | argument is definitive. Read it and you will learn | something new. It may not convince you otherwise on the | topic but it does show how baseless your "science" claims | are given that you don't fully understand it yourself. | pavlov wrote: | Were the pyramids of Giza built by aliens? Well, it sure | looks that way if you focus exclusively on evidence | that's open to your preferred interpretation... And as | for the all opposing evidence, nobody can disprove that | it's just the aliens trying to hide their tracks. | | Machine cognition is a similarly extraordinary claim | that's going to need a lot more evidence than a just- | right sequence of inputs and outputs. | oneoneonetwo wrote: | I don't know if you played with chatGPT but it's much | more than a just right sequence of inputs and outputs. | | I have already incorporated into my daily use (as a | programmer). It has huge flaws but the output is | anecdotally amazing enough that the claim of "cognition" | is not as extraordinary as you think it is. | | Especially given the fact that we don't even fully | understand what cognition is, the claim that it is NOT | cognition is equally just as crazy. | gillesjacobs wrote: | Let me falsify your claim immediately: the inputs of | these models are nothing like the inputs a human | receives, subword tokens do not even match up with | lexical items (visually, textually and semantically). | | You seem to agree with me even though your interpretation | of falsifiability is inverted: I am not asking that | authors make a claim that their models do not mimick | human intelligence. Like OP, I ask them that they do not | make that positive claim, i.e. omit humanising language | unless they can substantiate it with evidence. | oneoneonetwo wrote: | It's an invalid falsification. | | The input to chatGPT is a textual interface, the output | is letters on a screen. That is the exact same interface | as if I were chatting with a human. | | Your getting into the technicalities of intermediary | inputs and outputs. Well sure... analog data seen by the | nueral wetware of human brains IS obviously different | from the textual digital data inputted into the ML model. | HOWEVER, we are looking for an isomorphism here. Similar | to how a emulated playstation on a computer is very | different then a physical playstation... an internal | isomorphism STILL exists between hardware and the | software emulating the hardware. | | We do not know if such an isomorphism exists between | chatGPT and the human brain. This isomorphism is | basically the crystallized essence of what cognition is | if we could define it. If one does exists it's not | perfect there are missing things. But it is niave to say | that some form isomorphism isn't there AT ALL. It also | niave to say that there is FOR SURE an isomorphism. | | The most rational and scientific thing at this point is | to speculate. Maybe what chatGPT is, is something vaguely | isomorphic to cognition. Keyword: maybe. | | It is NOT an unreasonable speculation GIVEN what we KNOW | and DON'T KNOW. | joe_the_user wrote: | _People do this because mirroring cognition to machine learning | lends credence that their specific modeling mechanism mimicks | human understanding and so is closer "to the real thing"._ | | Doesn't this also involve people not having another category | aside from "cognition" to put natural language processing acts | in? How many neural net constructors have a rigorously | developed framework describing what "cognition" is? | | I mean, there's a common counter argument to the "this is not | cognition" position. That is: "you're just using 'cognition' as | a placeholder for whatever these systems can't do". I don't | think that counter-argument is true or characterizes the | position well but it's important to frame one's position so it | doesn't seem to be subject to this counter-argument. | gillesjacobs wrote: | > Doesn't this also involve people not having another | category aside from "cognition" to put natural language | processing acts in? | | Yes, of course this might be an even more primary reason; do | not attribute to malice what can be explained by laziness. | However, AI researchers should be wary of their language, | that point is hammered in most curricula I have seen. So at | the least it is negligence. | | > I mean, there's a common counter argument to the "this is | not cognition" position. That is: "you're just using | 'cognition' as a placeholder for whatever these systems can't | do". | | Very valid point, but we know current deep learning | mechanisms do not mimick human learning, language | understanding and production in any way. They are far too | simplified and specific for that. | | Neural network activation functions are a far cry from neural | spiking models and biological neural connectivity is far more | complex than the networks used in deep learning. The | attention mechanism that drives recent LLMs is also claimed | to have some biological similarities, but upon closer | inspection drawing strong analogies is not credible [1]. | computer vs. human visual recognition tasks it falls apart | and higher-level visual concepts. [2] | | 1. https://www.frontiersin.org/articles/10.3389/fncom.2020.00 | 02... | | 2. https://arxiv.org/abs/1906.08764 | gillesjacobs wrote: | Not to shoot across the bow of CS Engineers but the trend I | spot (tentatively) is that it is pure computer science folk | that most often do this. In NLP you have a mix of people coming | from pure CS and signal processing (the latter esp. in speech | processing) and others who come from linguistics or other | humanities. | | The CS people seem all too happy to humanise computation, | probably because they had less direct teaching regarding the | cognitive mechanisms behind cognition and language production. | Zababa wrote: | I'm not really sure about the context here, but I know that I | tend to humanize AIs, for example interacting with ChatGPT like | with a regular human being, because I'm being nice to him and | he's being nice to me in return. I don't know if it's more like | being nice to a human, or more like taking good care of your | tools so they will take good care of you, but it just feels | better for me. | nathan_compton wrote: | This will hardly seem like a controversial opinion, but LLM are | overhyped. Its certainly impressive to see the things people do | with them, but they seem pretty cherry-picked to me. When I sat | down with ChatGPT for a day to see if it could help me with | literally any project I'm currently actually interested in doing | it mostly failed or took so much prompting and fiddling that I'd | rather have just written the code or done the reading myself. | | You have to be very credulous to think for even a second that | anything like a human or even animal mentation is going on with | these models unless your interaction with them is anything but | glancing. | | Things I tried: | | 1) there are certain paradigms I find useful for game | programming. I tried to use ChatGPT to implement these systems in | my favorite programming language. It gave me code that generally | speaking made no sense. It was very clear that it did not | understand how code actually works. Eg: I asked it to use a hash | table to make a certain task more efficient and it just created a | temporary hash table in the inner loop which it then threw away | when the loop was finished. The modification did not make the | code more efficient than the previous version and missed the | point of the suggestion entirely, even after repeated attempts to | get it to correct the issue. | | 2) I'm vaguely interested in exploring SU(7) for a creative | project. Asked to generate code to deal with this group resulted | in clearly absurd garbage that again clearly indicated that while | ChatGPT can generate vaguely plausible text about groups it | doesn't actually understand anything about them. Eg: ChatGPT can | say that SU(7) is made of matrices with unit norm but when asked | to generate examples failed to generate any with this property. | | 3) A very telling experiment is to ask ChatGPT to generate logo | code that draws anything beyond simple shapes. Totally unable to | do so for obvious reasons. | | Using ChatGPT convinced me that if this technology is going to | disrupt anything, its going to be _search_ rather than _people_. | Its just a search engine with the benefit that it can do some | simple analogizing and the downside that it has no idea how | anything in the real world works and will confidently produce | total garbage without telling you. | Zababa wrote: | > This will hardly seem like a controversial opinion, but LLM | are overhyped. Its certainly impressive to see the things | people do with them, but they seem pretty cherry-picked to me. | When I sat down with ChatGPT for a day to see if it could help | me with literally any project I'm currently actually interested | in doing it mostly failed or took so much prompting and | fiddling that I'd rather have just written the code or done the | reading myself. | | > You have to be very credulous to think for even a second that | anything like a human or even animal mentation is going on with | these models unless your interaction with them is anything but | glancing. | | I've used ChatGPT, and I'd say it's right now as useful as a | google search, which is already a lot. Most humans would be | absolutely unable to help me (and probably you) for your | projects because they aren't specialized in that area. That's | not even talking about animals. I love my cats but they've | never really helped me when programming. | alsodumb wrote: | I hope ChatGPT in its current form will not be used for search. | As my friend says it, ChatGPT is not intelligent, it's just | capable of creating responses like it's knows everything. The | things it hallucinates is likely going to spread misinformation | and make it harder for the masses to search for true, factual | information. | | The other part is webtraffic: Google in theory could have | created an interactive, conversational style search engine | (with it without LLMs) if they wanted to, but a lot of websites | would have complained about Google taking away traffic from | them. I believe the same happened when Google started showing | it's own reviews instead of redirecting to Yelp. I wonder how | openAI or any LLM powered search is going to deal with it. They | don't have to worry about it anytime soon, they still have a | lot of time to get to a stage where they come anywhere close to | the number of queries Google handles in a day, but it'll be | interesting to see how things go. | nathan_compton wrote: | I agree that I'd still rather use a search engine over a | small set of sites than ChatGPT for exactly the reasons you | suggest and others. But I don't see ChatGPT as having a lot | of utility beyond functioning as a search interface for | credulous dummies. I mean if I were literally developing a | chatbot then clearly its a pretty interesting technology | (assuming its problems can be tamed or censored somehow), but | beyond that I don't really get it. | solidasparagus wrote: | The problem you are running into is that you are | overindexing on the fact that LLMs will sometimes be wrong | and you are used to using technology that is basically | always right. But we are in the early stages of LLM | adoption - correctness will improve (see for example | citation driven LLM-search) but more importantly, the set | of LLM-driven applications that can be probabilistically | correct and still wildly useful will grow. | | LLMs like ChatGPT are just so damn cheap for the power they | provide, it's inevitable | TeMPOraL wrote: | Thing is, ChatGPT is already incredibly useful for searching | random things you know enough about you can evaluate | responses critically. The alternative here is doing a regular | search, and wading through SEO-bloated, ad-laden content | marketing "articles". The quality and reliability of | information is about the same (or even favoring ChatGPT), but | without 90% of the text that's just filler, without bullshit, | ads, upsells, tracking scripts, etc. I tried it a few times | and it's a _much_ better experience than the web. I 'm gonna | be using it for as long as it lasts. | nathan_compton wrote: | Yeah, but its not as reliable as just restricting your | search to Wikipedia or the appropriate academic journals or | even chatting with a librarian! | TeMPOraL wrote: | Sure, when the topic matters or I need to study it in | depth, I can still go to Wikipedia or PubMed or Arxiv. | | But there are plenty of searches one does that are | trivial, or serve to illuminate the problem space, and | cover topics that in which I can rely on common sense to | correct wrong advice. And the issue with non-technical | topics, the kind applicable to mass audience - like e.g. | cooking or parenting or hygiene - are _very_ hard to | search about online, because all results are bullshit | pseudo articles written to drive traffic and deliver ads. | So it 's not that ChatGPT is so good, but more that | Internet for normal people is complete trash, and ChatGPT | nicely cuts straight through it. | b3morales wrote: | But if so this isn't because of its nature (the fact that | it's an LLM), but because of its inputs. An LLM fed the | same bullshit pseudo articles you refer to would likewise | spit out more bullshit. If ChatGPT works it's because its | sources have been carefully curated. | TeMPOraL wrote: | Fair. But the practical reality right now is that ChatGPT | delivers useful results without the noise, whereas normal | web search does not. It blows the web out of the water | when it comes to value to effort ratio of generic web | searches. It won't last forever, but I'm enjoying it for | as long as I can. | Al-Khwarizmi wrote: | Indeed. If I could have the Google from 20 years ago, I | probably wouldn't be so impressed with ChatGPT as search | engine. | | But with the Google (and the web) of today, where it's | practically impossible to find reliable information about | many subjects without adding "site:reddit.com" or | "wikipedia", I find it extremely useful. | albystein wrote: | The problem of hallucination in LLMs is a well-known and | studied problem and solutions have been proposed to counter | it. The most promising one is augmenting LLMs with a | retrieval system. This involves sourcing a large database of | factual information, say journal articles, over which the LLM | uses an information retrieval system(search engine) to | extract information on which its generated output is | conditioned. Recent job postings from OpenAI suggest that's | their next step of development for these LLMs. | | I think critics of these LLMs are missing the point about the | excitement around them. People are excited because of the | rate of progress/improvement from just two years or a year | ago. These systems have come a long way, and if you | extrapolate that progress into the future, I predict majority | of these shortcomings getting resolved | genidoi wrote: | The difference in wether you think ChatGPT is game changing or | another overhyped LLM seems to come down to: | | 1) do you acknowledge prompt engineering is a real skill set? | | 2) are you willing to improve your prompt engineering skill set | through research and iteration? | | There is much to learn about prompt engineering from that | "Linux VM in ChatGPT" post and other impressive examples (where | the goal of is to constrain ChatGPT to only engage in a | specific task) | axg11 wrote: | I disagree that LLMs are overhyped, but it's very subjective. | Are current LLMs a few steps from AGI? No. Will LLMs change the | computing landscape? Yes, I believe they will. | | ChatGPT, without any major changes, is already the best tool | out there for answering programming questions. Nothing else | comes close. I can ask it to provide code for combining two | APIs and it will give useful and clean output. No need to | trudge through documentation, SEO-hacked articles, or 10 | different Stack Overflow answers. Output quality will only | improve from here. Does it sometimes make mistakes? Yes. There | are also mistakes in many of the top SO answers, especially as | your questions become more obscure. | | Aside from programming, how many other fields are there where | LLMs will become an indispensable tool? I have a PhD and | ChatGPT can write a more coherent paragraph on my thesis topic | than most people in my field. It does this in seconds. If you | give a human enough time, they will be able to do better than | ChatGPT. The problem is, we're already producing more science | within niche scientific fields than most scientists could ever | read. As an information summary tool, I think LLMs will be | revolutionary. LLMs can help individuals leverage knowledge in | a way that's impossible today and has been impossible for the | last 30 years since the explosion in the number of scientific | publications. | nathan_compton wrote: | It can reproduce a statistically plausible paragraph, | certainly. But there is a great deal more to research than | producing statistically plausible paragraphs. It doesn't | _understand_ anything! | | I've actually worked on a project where there have been | attempts to use GPT like models to summarize scientific | results and the problem is it gets shit wrong all the time! | You have to be an expert to separate the wheat from the | chaff. It operates like a mendacious search engine pretending | to be a person. | visarga wrote: | The problem is that we need to pair generative models with | verification systems. We have the models, but no | verification yet. Fortunately code and math are easier to | verify. Some things require simulation. In other cases you | can substitute an ensemble of solutions & picking the most | frequent answer as consistency based verification. But for | each domain we need to create verifiers and that will take | some time. | | The good thing is that we'll be able to generate training | data with our models by filtering the junk with the | verifiers. Then we can retrain the models. It's important | because we are getting to the limit of available training | data. We need to generate more data, but it's worthless | unless we verify it. If we succeed we can train GPT-5. | Human data will be just 1%, the race is on to generate the | master dataset of the future. I read in a recent paper that | such a method was used to improve text captions in the | LAION dataset. https://laion.ai/blog/laion-5b/ | lambdatronics wrote: | >we need to pair generative models with verification | systems >code and math are easier to verify | | I would love to see a two-stage pipeline using a LLM to | convert natural language specifications into formal | specifications for something like Dafny, and then follow | up with another model like AlphaZero that would generate | code & assertions to help the verifier. This seems like | something that a major group like DeepMind or OpenAI | could pull off in a few years. | goatlover wrote: | One concern here is that if ChatGPT replaces the need to go | to websites like Stack Overflow or Wikipedia, what happens to | them? Do they stick around if the only people who visit them | are there to feed new stuff to chatGPT? Also, how does | chatGPT get hold of papers and articles behind pay walls? How | much of the scientific publications are free? | macrolocal wrote: | Points taken, but LLMs are still outpacing expert predictions, | so empirically they're under-hyped. | btbuildem wrote: | It is very, very good with language, and very bad with facts | and numbers. That's an oversimplification, but also the gist of | it. | | You have to recognize how it works, why it works - then you can | use it as basically an incredible superpower force multiplier. | monkmartinez wrote: | I disagree and think this is a very controversial opinion. | | Playing around with it last night convinced me that LLM's are a | huge, game changing technology. I was trying to decide which | material to use for an upcoming project. The model doesn't use | the internet without some hacking, so I had it write a program | in python using the tkinter UI kit. | | I asked it to create a UI with input boxes for material, weight | of material, price and loss due to wastage. The program takes | all of those inputs and converts the material into grams from | KG, pounds, ounces. It then calculates the price per gram and | takes a loss percentage (estimate given by user). It then | writes a text file and saves it to a directory. | | I literally pasted the code into VS code and had to change | Tkinter to tkinter. Hit run and it worked flawlessly. I have | NEVER used tkinter and it took about 30 minutes from start to | finish. | | This morning, I asked my 9th grade son what he is learning in | 9th grade biology. He told me he is learning cellular | endocytosis. I asked chapGPT to explain endocytosis like I was | a 5 year old and read it to him... he says; "Ask it to explain | it like a scientist now." After that he said it was a really | good and we started asking it all kinds of biology questions. | | I happen to agree that search will be the first thing | disrupted. However, I think simply saying "search" doesn't come | close to capturing how deep this will change the way we think, | use and progress in terms of the way we define "search" right | now. | nathan_compton wrote: | I've got a young kid and I'd think twice before letting this | model explain any science to him. If your criteria for | whether a model is good is "it fooled a 9th grader" well, I | don't know what to tell you. | | I think you have a point about your tkinter example. That | kind of stuff _is_ a lot more convenient than googling and | copying and pasting code. But if you push it beyond stuff | that you could easily find on stack exchange or in | documentation somewhere it doesn't work that well. Like I | said, its a search engine with a lot of downsides and some | upsides. | marcinzm wrote: | > If your criteria for whether a model is good is "it | fooled a 9th grader" well, I don't know what to tell you. | | Fooling a 9th grader is amazing. That's a pretty well | formed human being right there except with less life | experience. Fundamentally no different from you in general | reasoning terms except on a smaller set of information. So | fooling you is merely a question of model size. | radford-neal wrote: | "Fool" is the operative word here. ChatGPT is quite | capable of producing very plausible sounding text about | biology that is totally incorrect. See, for example, the | example in my comment at https://www.lesswrong.com/posts/ | 28XBkxauWQAMZeXiF/?commentId... | marcinzm wrote: | You're basically complaining that a single model doesn't | have full knowledge of every single area of all of human | knowledge. It's got decent knowledge of most areas | including programming with probably better overall | knowledge than a high school student. That's downright | amazing and probably more knowledge than any single human | actually has. The rest is likely a matter of improvement | along the same lines versus some radical redesign. | radford-neal wrote: | Well, I agree that it's amazing - it almost always | produces grammatical output, for instance. But it's not a | reliable way of obtaining knowledge. One should not, in | particular, try to learn about biology by asking ChatGPT | questions. It often produces made-up stuff that is just | wrong. And it's very confidently wrong, with the output | often coming across like someone barely concealing their | contempt that you might doubt them. | | It may or may not be fixable without radical redesign. | The underlying training objective of mimicking what | humans might say may be too at variance with an objective | of producing true statements. | sarchertech wrote: | My wife (a physician) asked it multiple medical questions | and the majority of the time they were dangerously wrong, | but looked perfectly fine to me. | | I asked it a series of questions about my area of | expertise and they were wrong but looked perfectly fine | to my wife. | | It even confidently "solved" the 2 generals problem with | a solution that looks completely plausible if you don't | already know that it won't work. | tshaddox wrote: | Maybe I'm just old, but there just isn't much that I want to | computers to tell me about that they don't already do a decent | job at. Everyone loves to complain about how bad Google search | is, but I very rarely find myself desperately looking for | something and unable to find it. There's certainly no normal | conversational interactions I can think of that I would love to | have with a computer but have been unable to before ChatGPT and | similar. | | That limits how impressed I can be by ChatGPT and similar | beyond just being impressed by it on a purely technical level. | And it's certainly very technically impressive, but not in some | transcendental way. It's also very impressive how could recent | video games with ray tracing look, or how good computers are at | chess, or how many really cool databases there are these days, | or how fast computers can sort data. | fourfivefour wrote: | I used chatGPT to solve a sqlite bug involving a query that was | taking 4 seconds to run. I pasted the query and it identified | many possible issues with the query including the offending | problem (it was missing an index on a timestamp). | | It also passed 3/4 of our companies interview process including | forging a resume that passed the recruiter filter. | | That being said, I COMPLETELY agree with you that chatGPT will | not disrupt anything. Your example cases are completely as | VALID as are my example cases. | | chatGPT is, however, the precursor to the thing that will | disrupt everything. | Al-Khwarizmi wrote: | Do my core work? No, it's not going to, at the moment. | | But it's already saving me nontrivial amounts of time on tasks | like "write a polite followup email reminding person X, who | didn't reply to the email I sent last week, that the deadline | for doing Y expires at date Z". | | I typically spend at least 3-4 minutes finding the words for | such a trivial email and thinking how to write it best, e.g. | trying to make the other person react without coming across as | annoying, etc. (Being a non-native English speaker who | communicates mostly in English at work may be a factor). | ChatGPT is really good with words. Using it, it takes a few | seconds and I can use the output with only trivial edits. | Jack000 wrote: | LLMs may be overhyped, but transformers in general are _under_ | hyped. | | LLMs make a lot of mistakes because they don't actually know | what words mean. The key thing is though - it's _much harder_ | to generate coherent text when you don 't know what the words | mean. In a similar vein it's completely unreasonable to expect | an LLM to perform visual tasks when it literally has no sense | of sight. | | The fact that it can kind of sort of do these things at all is | evidence of the super-human generalization potential of the | transformer architecture. | | This isn't very obvious for English because we have prior | knowledge of what words mean, but it's a lot more obvious when | applied to languages humans don't understand, like DNA and | amino acid sequences. | fourfivefour wrote: | How can these things not know what words mean? Did you not | see how they created a virtual machine under chatGPT? They | told it to imitate bash and they typed ls, and cat jokes.txt | and it outputted things completely identical to what you'd | expect. Look it up. https://www.engraved.blog/building-a- | virtual-machine-inside/ | | I don't see how you can explain this as not knowing what | words mean. It KNOWS. | hodgesrm wrote: | > This will hardly seem like a controversial opinion, but LLM | are overhyped. | | As the [excellent] paper points out, LLMs are complex functions | that can be embedded in systems to provide plausible answers to | a prompt. Here's the money sentence. LLMs are | generative mathematical models of the statistical distribution | of tokens in the vast public corpus of humangenerated text, | where the tokens in question include words, parts of | words, or individual characters including punctuation | marks. | | Rather than focus on the limitations of this approach to answer | general queries, which are manifest, it seems more interesting | to ask a different question. Under what circumstances do LLMs | give answers that are reliably equivalent to or better than | humans? The answer would: | | 1. Illuminate where we can use LLMs safely. | | 2. Direct work to make them better. | | It's already impressive that within certain scopes ChatGPT | gives very good answers, indeed better than most humans. | dragonwriter wrote: | > Under what circumstances do LLMs give answers that are | reliably equivalent to or better than humans? | | _Which_ humans? Humans give a... fairly wide range of | responses. | hodgesrm wrote: | I'm a history major and love classical history. My first | question to ChatGPT was: | | > Why was Julius Caesar murdered? | | The answer was the following, which would pass a standard | exam question on the topic. It exhibits [the appearance of] | multi-layer reasoning and has a nice conclusion. | | > Julius Caesar was assassinated on the Ides of March in 44 | BC by a group of Roman senators who believed he had become | too powerful and posed a threat to the Roman Republic. The | senators were concerned that Caesar had ambitions to become | king, which would have ended the Republic and given him | absolute power. The senators were also concerned about | Caesar's growing popularity with the people of Rome. They | believed that assassinating him was the only way to prevent | him from becoming too powerful and destroying the Republic. | | It's interesting to note that most of the evidence for this | answer including 2000 years of interpretation is available | in textual form _on the Internet_. It 's easily accessible | to LLMs. | TeMPOraL wrote: | _Average_ humans? Within 1 stdev from the mean? | dragonwriter wrote: | > Average humans? Within 1 stdev from the mean? | | This implies that performance has unqiue natural, | objective, ratio-level (or at least, a unique consistent | interval-level) measure. Otherwise the mean is, itself, | meaningless. | | "How well you answer a question" doesn't seem to fit | that, its maybe at best a (somewhat subjective, still) | ordinal quality, so the median (or mode) is the only | useful average. | | But I think you'll find that without restricting things | more than "humans", both the median and mode of responses | to most prompts is... quite bad. | nathan_compton wrote: | This reminds me that coding with ChatGPT felt like pair | programming with a not super smart person who could google | and type really fast. Not really fun! | armoredkitten wrote: | Please don't reduce LLM down to ChatGPT (or generative models | more generally). People are using LLM for real-world problems | every day. BERT and its descendants/variants are used all over | the place for many different problems in natural language | processing. I and my team have used it on dozens of different | projects, mainly in classifying text documents and inputs. And | it works very well. Multilingual LLMs are responsible for the | huge improvements in machine translation; my team has to deal | with text in multiple languages, and these models are vital | there too. We have used LLM on real-world problems that are in | production _now_ and are saving hundreds of person-hours of | tedious work. | | ChatGPT? Yeah, it's neat. I'm sure people will find some useful | niche for it. And I do think generative models will eventually | have a big impact, once researchers find good ways to ground | them to data and facts. This is already an active area of | research -- combining generative LLMs with info retrieval | methods, or targeting it to a specific context. (Meta just gave | a talk last week at the NeurIPS conference about teaching a | model to play Diplomacy, a game that mostly involves talking | and negotiating deals with the other players. ChatGPT is too | broad for that -- they just need a model that can talk about | the state of the game board.) So in general, I'm optimistic | about generative LLMs. But ChatGPT...is just a toy, really. | It's not the solution -- it's one of the signposts along the | way toward the real solution. It's a measure of progress. | hodgesrm wrote: | I wouldn't undersell ChatGPT. It's like a repl for a | particular LLM. Maybe there are others but it's the first | time many people have gotten direct access to the technology. | Sometimes the medium _is_ the message. | mikodin wrote: | Edit: I also see that I am falling prey to exactly what the | paper itself is talking about. | | "The more adept LLMs become at mimicking human language, the | more vulnerable we become to anthropomorphism, to seeing the | systems in which they are embedded as more human-like than they | really are. This trend is amplified by the natural tendency to | use philosophically loaded terms, such as "knows", "believes", | and "thinks", when describing these systems." | | -- | | An ignorant statement / question I have is why are you using it | write code? It's a chatbot, no? | | As you've mentioned, it's a really powerful search, and is like | having a conversation with someone who is literally the | internet. | | For example "What is the glycemic index of oatmeal?" | | "What is Eihei Dogen's opinion of the Self and how does it | differ from Bassui's?" | | I get highly detailed and accurate output with these. | | The first question is simple and the second is far from it. | It's breaking down two Zen masters experiences and comparing | them in an amazing way. | | I've been thoroughly impressed with Chat GPT so far. | | Ask it to breakdown the high level points of a book you've | read. | | Ask it to rewrite a song in the style of a different artist. | | It's so cool, I feel like I legitimately have an answer to any | random question at my finger tips and have to do zero filtering | for it. | nathan_compton wrote: | "An ignorant statement / question I have is why are you using | it write code? It's a chatbot, no? | | I've found it so incredibly useful to simply replace Google." | | Heard of Stack Exchange? | | I teach and I expect many students to use language models | like ChatGPT to do their homework, which involves writing | code. Lots of what people are doing with it is coding (there | have been quite a few posts here using it that way). | | I've actually also used ChatGPT for literary/song writing | experiments and it stinks, aesthetically. The lyrics it | wrote, even with a lot of prompting, were totally asinine. | And how could they not be? | RosanaAnaDana wrote: | I like the discussion, but this article 'feels' like more Luddite | goalpost moving, and is reflective of a continuous sentiment I | feel strains so much of the conversation around intelligence, | agentism, and ai going on today. | | I think that because we lack a coherent understanding of what it | means to be intelligent at an individual level, as well as what | it means to be an individual, we're missing much of the point of | what's happening right now. The new line in the sand always seems | to be justified based on an argument whose lyrics rhyme with | identity, individual, self, etc. It seems like there will be no | accepting of a thing that may have intelligence if there is no | discernable individual involved. Chomsky is basically making the | same arguments right now. | | I think we'll see something that we can't distinguish from hard | advanced general intelligence, prob in the next 3-5 years, and | probably still have not made any real advancement into | understanding what it means to be intelligent or what it means to | be an individual. | anyonecancode wrote: | Increasingly I don't think the question of "what is | intelligence" is so useful or relevant here. It feels a bit | like arguing over whether the "artificial horse" that started | appearing at the end of the 19th/beginning of the 20th C were | actually horses. Cars weren't, and still aren't, but that | misses the point. | | AI isn't intelligent, and never will be, and I don't think that | matters all that much. | RosanaAnaDana wrote: | I think I agree in sentiment, and I'm wondering what your ake | is on the article/ current discussions article. | | I guess my premise is that I don't think we have a useful | enough definition of intelligence because the ones I see | people writing articles on seem to be dependent or defined by | agency, and specifically humanish forms of agency. So I guess | your point would be "these systems aren't intelligent, but | that's not relevant"? I suppose I out the issue at the | currency of the definition of intelligence. It's seemed to be | very much synonymous with "how humans do things", making it | somewhat impossible to give charity to the arguments | presented in this paper with the caveats on "not | anthropomorphising". Like I can't compare these two things if | your definition of intelligence is fundementally based on | what "Anthros" do or do not do and simultaneously not engage | in anthropromorism. | | To follow on your point, if these things aren't displaying | "intelligence", but that's also not the point, what then are | they displaying? | | It seems to me this is a failure of introspection on the part | of AI philosophy to recognize how limited our understanding | of "HI" is. | anyonecancode wrote: | I think the question of "what is intelligence" is an | interesting one, and technology (especially computer | technology) gives us some interesting angles to look at it, | but I think it dominates the conversation | disproportionately to its importance. Things like ChatGPT, | and the technologies they presage, will absolutely have a | significant impact on society, economics, etc, but getting | tangled up in questions of "what is intelligence" impede | rather than help us to think through these implications and | prepare for them. | | Put another way -- I do not believe the future holds Blade | Runner replicants. If we're not careful, though, it does | hold Blade Runner corporations. While, philosophically, | it's interesting to ask if androids dream of electric | sheep, that question isn't very helpful in trying to nudge | the future in a more utopic rather than dystopic direction. | lambdatronics wrote: | Edsger Dijkstra: "The question of whether Machines Can Think | (...) is about as relevant as the question of whether | Submarines Can Swim." | plutonorm wrote: | I 100% agree. I would also add that most of the arguments are | driven by emotion. The truth is that we dont know what | intelligence means and we dont know what kinds of system have | intelligence. The only tools we have to measure intelligence | are those designed for humans. When we test the machines they | do better than terribly and they are improving very quickly. | There is no possible logical argument you can put forward | against their intelligence in the face of this evidence from | these human tests - because we cannot define intelligence in | any other way than these tests. Claims against intelligent | machines always boil down to 'obviously they aren't' and the | arguments have have to be this shallow simply because they have | no firm footing from which to base their argument. | Chirono wrote: | This paper, and most other places i've seen it argued that | language models can't possibly be conscious, sentient, thinking | etc, rely heavily on the idea that llms are 'just' doing | statistical prediction of tokens. | | I personally find this utterly unconvincing. For a start, I'm not | entirely sure that's not what I'm doing in typing out this | message. My brain is 'just' chemistry, so clearly can't have | beliefs or be conscious, right? | | But more relevant is the fact that llms like ChatGPT are only | pre-trained on pure statistical generation, followed by further | tuning through reinforcement learning. So ChatGPT is no longer | simply doing pure statistical modelling, though of course the | interface of calculating logits for the next token remains the | same. | | note: i'm not saying i think llms are conscious. I don't think | the question even makes much sense. I am saying all the arguments | that i've seen for why they aren't have been very unsatisfying. | goatlover wrote: | > I personally find this utterly unconvincing. For a start, I'm | not entirely sure that's not what I'm doing in typing out this | message. My brain is 'just' chemistry, so clearly can't have | beliefs or be conscious, right? | | Your brain is part of an organism who's ancestors evolved to | survive the real world, not by matching tokens. As such, | language is a skill that helps humans survive and reproduce, | not a tool used to mimic human language. Chemistry is the wrong | level to evaluate cognition at. | | Also, you can note the differences between how actual neurons | work compared to language models as other posters have | mentioned. | mrayder wrote: | For philosophical standpoint it would perhaps be wise to ask what | is the purpose of LLM's in general? | | Should they somehow help humans to increase their understanding | not only of the languages, their differences but also knowledge | of what is true and what isn't? | | Perhaps it could be said that if anything there are helpful as an | extension of humans imperfect and limited memory. | | Should the emphasis be put on improving the interactions between | the LMM's and humans in a way that they would facilitate | learning? | | Great paper written at the time when more humans have been | acquainted to LMM's due to technological abstraction and creation | of easily accessible interfaces. (openAI chat) | canjobear wrote: | I'll agree to stop saying LM's "think" and "know" things if you | can tell me precisely what those mean for humans. | goatlover wrote: | Maybe there isn't a precise definition, but clearly for humans | thinking and knowing is related to having bodies that need to | survive in the world with other humans and organisms, which | involves communication and references to external and internal | things (how your body feels and what not). This is different | from pattern matching tokens, even if it reproduces a lot of | the same results, because human language creates a lot of | patterns that can be matched. | | We could say both humans and LLMs are intelligent, but in a | different way. | hackinthebochs wrote: | >This is different from pattern matching tokens | | But is it different in essential ways? This is not so clear. | Humans developed the capacity to learn, think, and | communicate in service to optimizing an objective function, | namely fitness in various environments. But there is an | analogous process going on with LLMs; they are constructed | such that they maximize an objective function, namely predict | the next token. But it is plausible that "understanding" | and/or "intelligence" is within the solution-space of such an | optimization routine. After all, it's not like "intelligence" | was explicitly trained for in the case of humans. Nature has | already demonstrated emergent function as a side-effect of an | unrelated optimizer. | skybrian wrote: | There's a way to anthropomorphize large language models that I | think is less misleading: they are like a well-read actor that | always "wants" to play "let's pretend." LLM's are trained on | "fill in the blank" which means they follow the "yes, and" rule | of improv. They are very willing to follow your lead and to | assume whatever role is necessary to play their part. | | If you give them hints about what role you want by asking leading | questions, they will try to play along and pretend to hold | whatever opinions you might want from them. | | What are useful applications for this sort of actor? It makes | sense that language translation works well because it's | pretending to be you, if you could speak a different language. | Asking them to pretend to be a Wikipedia article without giving | them the text to imitate is going to be hit and miss since | they're just as willing to pretend to be a fake Wikipedia | article, as they don't know the difference. | | Testing an LLM to find out what it believes is unlikely to do | anything useful. It's going to pretend to believe whatever is | consistent with the role it's currently playing, and that role | may be chosen randomly if you don't give it any hints. | | It can be helpful to use prompt engineering to try to nail down a | particular role, but like in improv, that role is going to drift | depending on what happens. You shouldn't forget that whatever the | prompt, it's still playing "let's pretend." | [deleted] | RosanaAnaDana wrote: | Without reading the article or looking it up: What country is | south of Rwanda? | macrolocal wrote: | Have you seen Neptune Frost yet? I want that keyboard jacket. | schizo89 wrote: | The paper discusses how these models operate and state that | they're only predict next series of token while somehow human | intelligence works otherwise. The marxist ideology has the law of | the transformation of quantity into quality and vice versa -- | which was formed in 19th century and performance of these models | is just another proof of it. I would argue that _emerging_ | mechanics in AI models that we see with increased size of models | is no different than how our mind works. It's about emergence of | intelligence in complex systems -- and that a materialist | worldview central to the science. | CarbonCycles wrote: | This paper and a recent post by Sebastian Raschka (where he | decomposed a Forrester report about the uptake of technologies in | industry) is alluding to something I have witnessed in | system/control design and applied research. | | Both LLMs and massive CV architectures are NOT the holistic | solution. Rather, they are the sensors and edge devices that have | now improved both the fidelity and reliability to a point where | even more interesting things can happen. | | I present a relevant use case regarding robotic arm manipulation. | Before the latest SOTA CV algorithms were developed, the legacy | technology couldn't provide the fidelity and feedback needed. | Now, the embedded fusion of control systems, CV models, etc. we | are seeing robotic arms that can manipulate and sort items | previously deemed to be extremely difficult. | | Research appears to follow the same pattern...observations and | hypothesis that were once deemed too difficult or impossible at | that time to validate are now common (e.g., Einstein's work with | relativity). | | My head is already spinning on how many companies and non- | technical managers/executives are going to be sorely disappointed | in the next year or two that Stable Diffusion, Chat GPT, etc. | will deliver very little other than massive headaches for the | legal, engineering, recruiting teams that will have to deal with | this. | CrypticShift wrote: | > _sudden presence among us of exotic, mind-like entities might | precipitate a shift in the way we use familiar psychological | terms ... But it takes time for new language to settle, and for | new ways of talking to find their place in human affairs ... | Meanwhile, we should try to resist the siren call of | anthropomorphism._ | | Yes: Human analogies are not very useful because they create more | misunderstanding than they dissipate. Dumb ? Conscious ? No | thanks. IMO even the "i" in "AI" was already a (THE ?) wrong | choice. They thought we will soon figure out what Intelligence | is. Nope. Bad luck. And this "way of talking" (and thinking) is | unfortunately cemented today. | | However, I'm all for using other analogies more often. We need | to. They may not be precise, but if they are well-chosen, they | speak to us better than any technical jargon (LLM anyone ?), | better than that "AI" term itself anyway. | | Here is two I like (and never see much) : | | - LLMs are like the Matrix (yes that one !), in the | straightforward sense that they simulate reality (through | language). But that simulation is distorted and sometimes even | verges on the dream ( _" what is real? what is not?"_, says the | machine) | | - LLMs are like complex systems [1]. They are tapping into very | powerful natural processes where (high degree) order emerges from | randomness through complexity. We are witnessing the emergence of | a new kind of "entity" in a way strangely akin to | natural/physical evolutionary mechanisms. | | We need to get more creative here and stop that boring smart VS | dumb or human VS machine ping pong game. | | [1] https://en.wikipedia.org/wiki/Complex_system ___________________________________________________________________ (page generated 2022-12-10 23:00 UTC)