6 ms·

Stating this without any arguments is not very convincing. Perhaps you remember that language models were completely useless at coding some years ago, and now

• marcosdumay 1y ago
  Why state the same arguments everybody has been repeating for ages? LLMs can only give you code that somebody has wrote before. This is inherent. This is useful for a bunch of stuff, but that bunch won't change if OpenAI decides to spend the GDP of Germany training one instead of Costa Rica.
  • rhubarbtree 1y ago
    That’s not true. LLMs are great translators, they can translate ideas to code. And that doesn’t mean it has to be recalling previously seen text.
  • Epa095 1y ago
    First, how much of coding is really never done before? And secondly, what you say are false (at least if taken literally). I can create a new programming language, give the definition of it in the prompt, ask it to code something in my language, and expect something out. It might even work.
    • apwell23 1y ago
      > how much of coding is really never done before? A lot because we use libraries for 'done frequently before' code. i don't generate a database driver for my webapp with llm.
      • Epa095 1y ago
        We use libraries for SOME of the 'done frequently' code. But how much of enterprise programming is 'get some data from a database, show it on a Web page (or gui), store some data in the database', with variants? It makes sense that we have libraries for abstraction away some common things. But it also makes sense that we can't abstract away everything we do multiple times, because at some point it just becomes so abstract that it's easier to write it yourself than to try to configure some library. Does not mean that it's not a variant of something done before.
        • deleted 1y ago
          [deleted]
        • apwell23 1y ago
          > it's easier to write it yourself than to try to configure some library yeah unfortunately LLM will make this worse. Why abstract when you can generate. I am already seeing this a lot at work :(
        • sunrunner 1y ago
          > we can't abstract away everything we do multiple times I think there's a fundamental truth about any code that's written which is that it exists on some level of specificity, or to put it in other words, a set of decisions have been made about _how_ something should work (in the space of what _could_ work) while some decisions have been left open to the user. Every library that is used is essentially this. Database driver? Underlying I/O decisions are probably abstracted away already (think Netty vs Mina), and decisions on how to manage connections, protocol handling, bind variables, etc. are made by the library, while questions remain for things like which specific tables and columns should be referenced. This makes the library reusable for this task as long as you're fine with the underlying decisions. Once you get to the question of _which specific data is shown on a page_ the decisions are closer to the human side of how we've arbitrarily chosen to organise things in this specific thousandth-iteration of an e-commerce application. The devil is in the details (even if you know the insides of the devil aren't really any different).
          • skydhash 1y ago
    • vidarh 1y ago
      > I can create a new programming language, give the definition of it in the prompt, ask it to code something in my language, and expect something out. It might even work. I literally just pointed out the same time without having seen your comment. Second this. I've done this several times, and it can handle it well. Already GPT3.5 could easily reason about hypothetical languages given a grammar or a loose description. I find it absolutely bizarre that people still hold on to this notion that these languages can't do anything new, because it feels implausible that they have tried given how well it works.
      • lupire 1y ago
        Second, how much of commenting is really never done before?
        • apwell23 1y ago
          good question. why isn't the gp using llm to generate comments then.
          • vidarh 1y ago
            For some types of comment, it really would be tempting to automate the answers, because especially the "stochastic parrot" type comments are getting really tedious and inane, and ironically comes across as people parroting the same thing over and over instead of thinking. But the other answer is that often the value in responding is to sharpen the mind and be forced to think through and formulate a response even if you've responded to some variation of the comment you reply to many times over. A lot of comments that don't give me any value to read are comments I still get value out of through the process of replying to for that reason.
      • skydhash 1y ago
        If you give it the rules to generate something, why can't it generate it? That's what something like Mockaroo[0] does. It's just more formal. That's pretty much what LLM training does, extracting patterns from a huge corpus of text. Then it goes one to generate according to the patterns. It can not generate a new pattern that is not a combination of the previous one. [0]: https://mockaroo.com/ https://mockaroo.com/
    • bawolff 1y ago
      > First, how much of coding is really never done before? Lots of programming doesn't have one specific right answer, but a bunch of possible right answers with different trade-offs. The programmers job isn't just to get working code neccesarily. I dont think we are at the point where llm's can see the forest for the trees, so to speak.
  • nahbruheem 1y ago
    Generating unseen code is not hard. Set rules on what’s valid, which most languages already do; omit generation of known code; generate everything else The computer does the work, programmers don’t have to think it up. A typed language example to explain; generate valid func sigs func f(int1, int2) return int{} If that’s our only func sig in our starting set then it makes it obvious Well relative to our tiny starter set func f(int1, int2, int3) return int{} is novel This Redis post is about fixing a prior decision of a random programmer. A linguistics decision. That’s why LLMs seem worse than programmers because we make linguistics decisions that fit social idioms. If we just want to generate all the never before seen in this model code we don’t need a programmer. If we need to abide laws of a flexible language nature, that’s what a programmer is for; compose not just code by compliance with ground truth. That antirez is good at Redis is a bias since he has context unseen by the LLM. Curious how well antirez would do with an entirely machine generated Redis-clone that was merely guided by experts. Would his intuition for Redis’ implementation be useful to a completely unknown implementation? He’d make a lot of newb errors and need mentorship, I’m guessing.
    • nprateem 1y ago
      I think we're hoping for more than the 'infinite monkeys bashing out semantically correct code' approach.
      • nahbruheem 1y ago
        Ok, define what means and make it. Then as soon as you do realize you run into Gödel’s understanding your machine doesn’t solve problems related to its own existence and needs outside help. So you need to generate that yet unseen solution that lacks context for understanding itself… repeat and see it’s exactly generating one yet unseen layer of logic after another. Read the article; his younger self failed to see logic needed now. Add that onion peel. No such thing as perfect clairvoyance. Even Yann LeCun’s energy based models driving robots have the same experience problem. Make a computer that can observe all of the past and future. Without perfect knowledge our robots will fail to predict some composition of space time before they can adapt. So there’s no probe we can launch that’s forever and generally able to survive with our best guess when launched. More people need to study physical experiments and physics and not the semantic rigor of academia. No matter how many ideas we imagine there is no violating physics. Pop culture seems to have people feeling starship Enterprise is just about to launch from dry dock.
  • deleted 1y ago
    [deleted]
  • JoshCole 1y ago
    > LLMs can only give you code that somebody has wrote before. This premise is false. It is fundamentally equivalent to the claim that a language model being trained on a dataset: ["ABA", "ABB"] would be unable to generate, given input "B" the string "BAB" or "BAA".
    • 1718627440 1y ago
      Isn't the claim, that it will never make up "C"?
      • JoshCole 1y ago
        They don't claim that. They say LLMs only generate text someone has written. Another way you could refute their premise was by showing the existence of AI-created programs for which someone isn't a valid description of the writer (e.g., from evolutionary algorithms) then training a network on that data such that it can output it. It is just as trivial a way to prove that the premise is false. Your claim here is slightly different. You're claiming that if a token isn't supported, it can't be output [1]. But we can easily disprove this by adding minimal support for all tokens, making C appear in theory. Such support addition shows up all the time in AI literature [2]. [1]: https://en.wikipedia.org/wiki/Support_(mathematics) https://en.wikipedia.org/wiki/Support_(mathematics) [2]: In some regimes, like game theoretic learning, support is baked into the solving algorithms explicitly during the learning stage. In others, like reinforcement learning, its accomplished by making the policy a function of two objectives, one an exploration objective, another an exploitation objective. That existing cross pollination already occurs between LLMs in the pre-trained unsupervised regime and LLMs in the post-training fine-tuning via forms of reinforcement learning regime should cause someone to hesitate to claim that such support addition is unreasonable if they are versed in ML literature. Edit: Got downvoted, so I figure maybe people don't understand. Here is the simple counterexample. Consider an evaluator that gives rewards: F("AAC") = 1, all other inputs = 0. Consider a tokenization that defines "A", "B", "C" as tokens, but a training dataset from which the letter C is excluded but the item "AAA" is present. After training "AAA" exists in the output space of the language model, but "AAC" does not. Without support, without exploration, if you train the language model against the reinforcement learning reward model of F, you might get no ability to output "C", but with support, the sequence "AAC" can be generated and give a reward. Now actually do this. You get a new language model. Since "AAC" was rewarded, it is now a thing within the space of the LLM outputs. Yet it doesn't appear in the training dataset and there are many reward models F for which no person will ever have had to output the string "AAC" in order for the reward model to give a reward for it. It follows that "C" can appear even though "C" does not appear in the training data.
  • vidarh 1y ago
    > LLMs can only give you code that somebody has wrote before. This is inherent. This is trivial to prove to be false. Invent a programming language that does not exist. Describe its semantics to an LLM. Ask it to write a program to solve a problem in that language. It will not always work, but it will work often enough to demonstrate that they are very much capable of writing code that has never been written before. The first time I tried this was with GPT3.5, and I had it write code in an unholy combination of Ruby and INTERCAL, and it had no problems doing that. Similarly giving it a grammar of a hypothetical language, and asking it to generate valid text in a language that has not existed before also works reasonably well. This notion that LLMs only spit out things that has been written before might have been reasonable to believe a few years ago, but it hasn't been a reasonable position to hold for a long time at this point.
    • shrewduser 1y ago
      This doesn't surprise me, i find LLM's are really good at interpolating and translating. so if i made up a language and gave it the rules and asked it to translate i wouldn't expect it to be bad at it.
      • vidarh 1y ago
        It shouldn't surprise anyone, but it is clear evidence against the claim I replied to, and clearly a lot of people still hold on to this irrational assumption that they can't produce anything new.
        • skydhash 1y ago
          They're not producing anything new... If you give it the answer before asking the question, no wonder it can answer. Prompting is to find resonance in the patterns extracted from the training data, which is why it fails spectacularly for exotic programming languages.
          • vidarh 1y ago
            When you invent a language and tell it express something in that language, you've not given it the answer before asking the question. That's an utterly bizarre notion. The answer in question never existed before. By your definition humans never produce anything new either, because we always also extrapolate on patterns from our previous knowledge. > it fails spectacularly for exotic programming languages. My experience is that it not just succeeds for "exotic" languages, but for languages that didn't exist prior to the prompt. In other words, they can code at least simple programs even with zero-shot by explaining semantics of a language without giving them even a single example of programs in that language. Did you even read the comment you replied to above? To quote myself: "Invent a programming language that does not exist." I've had this work both for "from scratch" descriptions of languages by providing grammars, and for "combine feature A from language X, and feature B from language Y". In the latter case you might have at least an argument. In the former case you do not. Most humans struggle with tasks like this - you're setting a bar for LLMs most humans would fail to meet.
• Retric 1y ago
  Progress sure, but the rate the’ve improved hasn’t been particularly fast recently. Programming has become vastly more efficient in terms of programmer effort over decades, but making some aspects of the job more efficient just means all your effort it spent on what didn’t improve.
  • mirsadm 1y ago
    The latest batch of LLMs has been getting worse in my opinion. Claude in particular seems to be going backwards with every release. The verbosity of the answers is infuriating. You ask it a simple question and it starts by inventing the universe, poorly
  • lexandstuff 1y ago
    People seem to have forgotten how good the 2023 GPT-4 really was at coding tasks.
• apwell23 1y ago
  > Perhaps you remember that language models were completely useless at coding some years ago no i don't remember that. They are doing similar things now that they did 3 yrs ago. They were still a decent rubber duck 3 yrs ago.
  • vidarh 1y ago
    And 6 years ago GPT2 had just been released. You're being obtuse by interpreting "some years" as specifically 3.
  • deleted 1y ago
    [deleted]
• bigstrat2003 1y ago
  > Perhaps you remember that language models were completely useless at coding some years ago, and now they can do quite a lot of things, even if they are not perfect. IMO, they're still useless today, with the only progress being that they can produce a more convincing facade of usefulness. I wouldn't call that very meaningful progress.
  • IshKebab 1y ago
    They are very clearly not useless. You haven't given them a fair shake.
  • drdeca 1y ago
    I’ve found them somewhat useful? Not for big things, and not for code for work. But for small personal projects? Yes, helpful.
    • conradkay 1y ago
      It's funny how there's a decent % of people at both "LLMs are useless" and "LLMs 3-10x my productivity"
      • otabdeveloper4 1y ago
        > LLMs 3-10x my productivity x10 of zero is still zero, I guess.
  • wvenable 1y ago
    I don't know how someone can legitimately say that they're useless. Perfect, no. But useless, also no.
    • addaon 1y ago
      > I don't know how someone can legitimately say that they're useless. Clearly, statistical models trained on this HN thread would output that sequence of tokens with high probability. Are you suggesting that a statement being probable in a text corpus is not a legitimate source of truth? Can you generalize that a little bit?