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GPT-4 performs significantly worse on coding problems not in its training data
- Decabytes 4y agoI’m always asking ChatGPT about questions related to Smalltalk and Scheme and it does a terrible job with them. To be expected, but makes me wonder if it will ever be good enough for more niche programming languages and aspects of programming
- meh8881 4y agoThis guy seems to laugh away the fact that he gave the prompt in terribly broken and chunked up formats. I don’t think it’s surprising the model did poorly. Maybe the contamination issue is true. But maybe it’s also true that the model does fine on novel codeforce problems when you don’t feed it a garbage prompt?
- famouswaffles 4y agoOn code gen with some reflection baked in (ie feedback and memory), it shoout up to 88% (from 67%). There's some contamination to be sure but also still quite a lot to be done to better output
- YeGoblynQueenne 4y ago>> I don’t think it’s surprising the model did poorly. But it did poorly only on the problems it hadn't seen before. Was it prompted differently on one kind of problem, compared to the other?
- meh8881 4y agoBut you can do a task you’ve done before with poor specification too. Sure, maybe it is contamination. But who cares? We only ought to judge the tool on its performance for carrying out good instructions.
- contravariant 4y agoUnless the formatting is somehow very different for pre 2021 problems it is still a strong signal that it is (up to a point) just parroting a solution it had heard somewhere rather than inferring it some way else. This is neither good nor bad, that will depend on what you want to use the model for.
- meh8881 4y agoThat’s not my point. Maybe it is parroting a solution for something it has seen before, but still capable of writing solutions to problems it has not seen before when they’re well specified. The presence of contamination does not mean the general capability is intrinsically poor.
- krackers 4y agoThe prompting might be an issue, but I think the larger sign that it's not quite there yet in terms of symbolic reasoning is that even based on their own paper, gpt-4 gets only a 30 on AMC 10 (out of 150), whereas leaving the test blank would get you a 37.5. And this is on a closed-ended multiple choice-test, so the conditions should be favorable for it to dominate. Edit: Although this might be unfair considering that LLMs are known to be poor at calculation. (Maybe it would do better at proof-style, like USAMO?). I wonder how well chatGPT with WA integration would do.
- jprete 4y agoThe need for prompt engineering is an indicator of GPT failing the problem, not succeeding at it.
- bamboozled 4y agoThe need for “tools” is a bit of a red flag too.
- hughesjj 4y agoWhy? I'm significantly less productive and competent without tools, and this applies to every human I've ever known.
- sdenton4 4y agoLike how the need for roads is an indication that cars don't solve transportation problems. People adapt to maximize the utility of their tools; always have, always will.
- meh8881 4y agoI disagree. We cannot expect these tools to work if we don’t communicate what we want from them. Can’t do that for humans either. We shouldn’t be confusing clear communication with “prompt engineering”.
- tarruda 4y agoMaybe the OP is also contaminated a bias of wanting to find errors in GPT-4? Finding flaws in GPT-4 and ignoring the fact that we are at the dawn of AI, that seems like a good remedy to calm the AI existential crisis anxiety nowadays.
- hsuduebc2 4y agoThanks for little bit of hopium. But we are still doomed.
- deleted 4y ago[deleted]
- letitgo12345 4y agoI guess the meaning of our existence was always handing the universe over to the AI overlords lol
- lamp987 4y agoIf youre doomed by a fancy markov chain chatbot, you were never gonna make it in the first place.
- antibasilisk 4y agoPlease stop using this 'fancy markov chain' cope, it just makes you sound like you have absolutely no awareness of anything.
- chatmasta 4y ago> makes you sound like you have absolutely no awareness of anything almost like a fancy markov chain? :)
- RhysU 4y agoMost of us are much worse on coding problems not in our training set! (Looks down at dynamic programming problem involving stdin/stdout and combining two data structures).
- whatshisface 4y agoThe reason we're being kept around still is that you can solve the problem without it ever appearing in your training set, and once you have, it has.
- toomuchtodo 4y agoHints of The Nine Billion Names of God for sure. https://en.wikipedia.org/wiki/The_Nine_Billion_Names_of_God https://en.wikipedia.org/wiki/The_Nine_Billion_Names_of_God
- dymk 4y agoOh wow - Unsong [1] must have taken some inspiration from that. Into the queue it goes! [1] https://unsongbook.com/ https://unsongbook.com/
- Zurrrrr 4y agoWhat is that exactly? The site doesn't say.
- LordDragonfang 4y agoA work of online serial fiction by blogger Scott Alexander, formerly of Slate Star Codex, now of Astral Codex Ten. Goodreads has a decent intro blurb: https://www.goodreads.com/fr/book/show/28589297-unsong https://www.goodreads.com/fr/book/show/28589297-unsong If you're unsure whether his writing style is your thing, feel free to sample his shorter fiction from his blog: https://slatestarcodex.com/2015/06/02/and-i-show-you-how-deep-the-rabbit-hole-goes/ https://slatestarcodex.com/2015/06/02/and-i-show-you-how-dee... https://slatestarcodex.com/2015/04/21/universal-love-said-the-cactus-person/ https://slatestarcodex.com/2015/04/21/universal-love-said-th... https://astralcodexten.substack.com/p/idol-words https://astralcodexten.substack.com/p/idol-words
- dboreham 4y agoAre we ready to hand over the lollipop yet?
- andsoitis 4y ago[flagged]
- letitgo12345 4y agoWill take a bit of time before AI can consistently beat us on coding/proofs but the raw ingredients imo are there. As someone who was skeptical of AGI via just scaling things up even after GPT-3, what convinced me was the chain of thought prompting paper. That shows the LLM can pick up on abstract thought and reasoning patterns that humans use. Only a matter of time before it picks up on all of our reasoning patterns (or maybe it already has and is just waiting to be prompted properly...), is hooked up to a good memory system so it's not limited by the context window and then we can watch it go brrrr It can still make stupid mistakes in reasoning but I don't think that's fundamentally unsolvable in the current paradigm
- danielheath 4y ago> That shows the LLM can pick up on abstract thought and reasoning patterns that humans use. Does it? I’m still unconvinced it’s more than copying other examples of “show your work”.
- letitgo12345 4y agoIt's definitely not just copying verbatim. If you mean it's emulating the reasoning pattern it sees in the training data well...don't humans do that as well to get answers to novel problems?
- sidlls 4y agoWe don't know all the different ways humans arrive at answers to novel problems. And while these LLMs aren't literally just copying verbatim, they are literally just token selection machines with sophisticated statistical weighting algorithms biased heavily towards their training sets. That isn't to say they are overfitted, but the sheer scale/breadth gives the appearance of generalization without the substance of it.
- Izkata 4y agoHere's an argument that GPT does actually build an internal representation of the game Othello, it's not just token selection: https://thegradient.pub/othello/ https://thegradient.pub/othello/
- helf 4y ago[dead]
- Sparkyte 4y agoBecause training data is just weighted data for accuracy. It is like holding two balls one vibrant blue the other navy. You ask it to pick the blue ball and it goes constant to the vibrant one. This is because it is accurately blue. Sure it is some aspect of AI. But hopefully we don't go down the path of assuming it is sentient or capable of decision making on the basis of emotional intelligence.
- hesdeadjim 4y agoI threw a challenging rendering problem at it and I was pretty impressed with the overall structure and implementation. But as I looked deeper, the flaws became apparent. It simply made up APIs that didn’t exist, and when prompted to fix it, couldn’t figure it out. Still, despite being fundamentally wrong it did send me down some different paths.
- splatzone 4y agoI’ve had very good results from running the code and pasting the errors back into ChatGPT and asking it what to do. Sometimes it corrects itself quite well
- behnamoh 4y agoPut that in a loop and see if AGI emerges.
- rel2thr 4y agoyea I find it to be wrong a lot when coding But its faster for me to fix existing code than to write code from scratch so its still better than nothing for me
- osteele 4y agoSame. It seems similar to Copilot in that regard, but better at text-to-code, porting between languages or frameworks, and generating test cases and readmes: https://notes.osteele.com/gpt-experiments/using-chatgpt-to-port-a-python-package-to-javascript https://notes.osteele.com/gpt-experiments/using-chatgpt-to-p...
- jasfi 4y agoUsing APIs that don't exist is the biggest problem I've seen with ChatGPT, and it seems GPT-4 as well.
- vitorgrs 4y agoOne of the advantages of Bing, and do guess now ChatGPT with browsing plugin, is that it's able to search on the web for the right API.
- xwdv 4y agoI love how AI apologists trip over themselves to explain away deficiencies in GPT and explain how it will “get better with time”. Sorry, it won’t. This might even be peak GPT. Training data comes from human content, and currently there is decades worth of pure human content available. But new content will come in slowly, and it will probably take decades just to double the amount of training data we have today. Worse, since we expect AI generated content to start becoming widespread and difficult to distinguish from human content, GPT will eventually start cannibalizing its own outputs as training data, leading to shittier and shittier models over time that get overfitted and no longer seem human enough for any practical use. It’s a fading dream.
- krackers 4y agoIt's a good point for text input, but if you go multi-modal and somehow find a way to make good use of audio and video, there's practically unlimited data available. Also considering that humans will probably still only publish the output that looks good, even that still provides a weak signal on quality.
- jprete 4y agoI'm skeptical that multimodal input can help with programming or logic problems - or even most scientific problems.
- serverholic 4y ago[dead]
- mrbungie 4y agoHaving diagrams (think free body diagrams in static mechanics, or a T-s diagram in thermodynamics) make a lot of non-trivial problems a lot simpler to communicate. And correctly understanding an unambiguous definition of a problem is a major step towards solving it. If language was enough (or a similar idea, that multimodal input is not useful), college math professors wouldn't use so much chalk making drawings and diagrams to explain their ideas.
- waf 4y agoI've been adding C# code completion functionality to my REPL tool, and ended up reverting to the text-davinci model. The codex (discontinued?) and text-davinci models gave much better results than GPT3.5-turbo, specifically for code completion scenarios. The latest models seem to produce invalid code, mostly having trouble at the boundaries where they start the completion. My suspicion is that these latter models focus more on conversation semantics than code completion, and completing code "conversationally" vs completing code in a syntactically valid way has differences. For example, if the last line of code to be completed is a comment, the model will happily continue to write code on the same line as the comment. Not an issue in a conversation model as there is a natural break in a conversation, but when integrating with tooling it's challenging. Most likely the issue is that I'm not yet effective at prompt engineering, but I had no issues iterating on prompts for the earlier models. I'm loving the DaVinci model and it's working really well -- I just hope it's not discontinued too soon in favor of later models.
- pffft8888 4y agoI was just talking about this the other day: > it's more hacking than crareful and well specified engineering, and that could lead down a path of instability in the product where some features get better while others get worse, without understanding exactly why. https://news.ycombinator.com/threads?id=pffft8888&next=35269862 https://news.ycombinator.com/threads?id=pffft8888&next=35269...
- wakahiu 4y agoI can corroborate that text-davinci gives much better results than for tasks involving summarization or extraction of key sentences among a large corpus. I wonder what empirical metrics OpenAI uses to determine performance benchmarks for practical tasks like these. You can see the model in action for analysis of reviews here: https://show.nnext.ai/ https://show.nnext.ai/ [Disclaimer - I work at nnext.ai]
- woeirua 4y agoDoes this even matter? Sure GPT4 can’t solve every conceivable problem out there, but if it can solve the overwhelming majority of problems that humans have already seen then that is a huge improvement over what we had before.
- HeavyFeather 4y agoI wouldn’t say it doesn’t matter. You can say it’s a weakness that can be improved.
- the_only_law 4y ago> but if it can solve the overwhelming majority of problems that humans have already seen then that is a huge improvement over what we had before “the overwhelming majority of problems humans have” are not going to solved by generating code. At this point these LLMs are feeling like blockchain a few years back in the sense people were trying to tell me it was going to solve every problem that’s existed since the dawn of human civilization.
- polskibus 4y agoI wonder if he tried the trick that Microsoft recommended in their GPT4 evaluation paper, that is ask gpt to go step by step with explanations. It tends to produce much better results, simply because it is more fitting to the prediction mechanism that GPT uses . It tends to predict better when steps are smaller.
- DougBTX 4y agohttps://arxiv.org/pdf/2205.11916.pdf https://arxiv.org/pdf/2205.11916.pdf
- nopinsight 4y ago“Reflection-Based GPT-4 Agent is State-of-the-Art on Code Gen Iteratively refines code, shifting “accuracy bottleneck” from correct code gen to correct test gen HumanEval accuracy: -Reflexion-based GPT-4 88% -GPT-4 67.0% -CodeT 65.8% -PaLM 26.2%” with link to code in the Tweet: https://mobile.twitter.com/johnjnay/status/1639362071807549446 https://mobile.twitter.com/johnjnay/status/16393620718075494... 21% improvement after adding a feedback loop and self-reflection to GPT-4, which just went public 12 days ago. (The approach is based on a preprint published 4 days ago.) Human coders often need a feedback loop and self-reflection to properly “generate” code for problems novel to them as well. ----- A larger question: Are we hurling ourselves toward a (near) future of unaligned AGI with self-improvement capabilities?
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- riku_iki 4y agoso how do we know chat gpt didn't have humaneval in training data?
- nopinsight 4y agoThe point is adding a couple components can improve GPT-4 significantly, as shown above. The data it originally trained with is presumably held constant in the evaluation above.
- riku_iki 4y agothe point is if humaneval was in gpt training data, then this component improved memorization from mediocre to Ok-ish, and actual coding skills still not tested.
- nopinsight 4y agoFrom other people’s and my experiences, GPT-4 can do more than simply memorizing. It can at least interpolate and reason a little bit too. A few other tests show that GPT-4 would achieve much better results than 67% for something it has sufficient training data on like GRE Verbal and AP Macroeconomics. https://openai.com/research/gpt-4 https://openai.com/research/gpt-4 Yes, it still can’t generalize properly outside its training distribution. However, when armed with feedback and self-reflection, it seems better at that too.
- kozikow 4y agoI'm confident that if chatgpt trained on all code submitted to topcoder and code forces, then iteratively refined responses by compiling and evaluating the output, it could get to red topcoder quite easily. Just that programming challenges probably weren't that high of a priority and data is in silos.
- gateorade 4y agoThis has been my experience. I’m really impressed by how well GPT-4 seems to be able to interpolate between problems heavily represented in the training data to create what feels like novelty, eg. Creating a combination of pong and conway’s game of life, but it doesn’t seem to be good at extrapolation. The type of work I do is highly niche. I’ve recently been working on a specific problem for which there are probably only a hundred at most implementations running on production systems, all of them highly proprietary. I would be surprised if there were any implementations in GPTs training set. With that said, this problem is not actually that complicated. A rudimentary implementation can be done in ~100 lines of code. I asked GPT-4 to write me an implementation. It knew a decent amount about the problem (probably from Wikipedia). If it was actually capable of something close to reasoning it should have been able to write an implementation, but when it actually started writing code it was reluctant to write more than a skeleton. When I pushed it to implement specific details it completely fell apart and started hallucinating. When I gave it specific information about what it was doing wrong it acknowledged that it made a mistake and simply gave me a new equally wrong hallucination. The experience calmed my existential fears about my job being taken by AI.
- sterlind 4y agoI had a similar experience. I wanted it to write code to draw arcs on a world map, with different bends rather than going on a straight bearing. I did all the tricks, told it to explain its chain of thought, gave it a list of APIs to use (with d3-geo), simplified and simplified and spent a couple hours trying to reframe it. It just spit out garbage. Because (afaict) there aren't really examples of that specific thing on the Internet. And it's just been weirdly bad at all the cartography-related programming problems I've thrown at it, in general. And yeah, I'm much less worried about it replacing me now. It's just not.. lucid, yet.
- noduerme 4y agoI imagine that creative approaches to spacial problem solving would be one of the harder areas for it - not just because there are by definition fewer public examples of one-off or original solutions, but also because one has to visualize things in space before figuring out how to code it. These bots don't have a concept of space. I'm thinking of DALL-E (et. al) having problems with "an X above Y, behind Z".
- dorkwood 4y agoI feel vindicated reading this. Yesterday in a separate thread I claimed that it was wrong on 80% of the coding problems I gave it, and received the response from multiple readers that I was probably phrasing my questions poorly. I started to believe them, too. Unfortunately, my brain is structured in such a way that a unanimous verdict from a few strangers is enough to make me think I’m probably the one who’s wrong. I need to make note of these events as a way to remind myself that this isn’t always the case.
- IIAOPSW 4y agoWoah. I've never seen someone so self aware of the asch conformity test without literally talking about the asch conformity test. https://en.wikipedia.org/wiki/Asch_conformity_experiments https://en.wikipedia.org/wiki/Asch_conformity_experiments
- anonytrary 4y agoVindicated and excited. Gradient descent is likely not enough. I love it when we get closer to something but are still missing the answer. I would be very happy if "add more parameters and compute" isn't enough to get us to AGI. It means you need talent to get there, and money alone will not suffice. Bad news for OpenAI and other big firms, good news for science and the curious. I imagine physicists got very excited with things like the ultraviolet catastrophe, and the irreconcilable nature of quantum mechanics and general relativity. It's these mysteries that keep the world exciting.
- BoorishBears 4y agoThere's something ironic about implying that us not having a path to AGI is good news for the curious. If you're supervisiously curious then sure, we need to unlock another piece of a puzzle, more puzzle pieces means more puzzle solving. But if you're able to actually take a step back, AGI would be the the ultimate source of new puzzles for the curious. We don't even all agree on how to define the "GI", approaching AGI wouldn't be unlike meeting extraterrestrial life sitting on a computer.
- 4y ago
- visarga 4y agoSo it only does great at everything we've ever done? We only need to demonstrate once a new approach, and it eats that too.
- jokoon 4y agoYeah, I'm an AI skeptic. I'm suspecting some people try to make money but they cannot uphold their promise.
- choeger 4y agoOn top of that, I think we might have reached peak training data. Or rather, peak human-generated training data. From here on AI will produce much more data than humans. Consequently, it should become ever harder to train a model on something novel. I even wonder if humans in 50 years will recognize AI by its particular (early 21st century) style to write and speak.
- kweingar 4y ago> I even wonder if humans in 50 years will recognize AI by its particular (early 21st century) style to write and speak. I wonder if writing and speaking style will be frozen in time if people are bombarded with AI output all day.
- deleted 4y ago[deleted]
- ReptileMan 4y agoThe fact that it performs at all is amazing. It's like the old dog playing a piano joke.
- seu 4y ago"Model designed to guess next tokens from a sequence, based on its training data, fails to guess next tokens from sequences not similar to its training data." What's the news?
- BiteCode_dev 4y agoUsain Bolt performs significantly worse in a marathon than in a sprint. Yes.
- rakejake 4y agoOne another example I encountered today. <Prompt> show me an example of an enum LifecycleStatus deriving from an abstract class Status with fields status_type String and reason String (with default getters). Use a lombok builder on the enum class and show an example of building a LifecycleStatus enum setting only the status_type. </Prompt> Answer: ChatGPT tells me it absolutely is possible, and generates the code as well. Except, java Enums cannot derive an abstract class (since they implicitly derive Enum<T>). You can only have an enum implement an interface. Here, I realised that I had not mentioned the word "java" in this prompt (though I had started off the chat with the word "java" so I assume it was in the context. In any case, my next prompt was: <Prompt> Is the code example you shared for java? </Prompt> Answer: Yes, the code example I shared is for Java. Specifically, it is for Java 8 or newer, as it uses the @Builder annotation from Lombok, which was introduced in version 1.16.8 of Lombok. I continued the conversation and had it try out other methods of achieving the same thing. (abstract class with static inner builder class, enums implementing an interface). But it basically follows your prompt and provides whatever you ask it to provide. When I asked it to have an enum derive an abstract class, it did that for me without any complaints. When I later said, this was not possible, it changed tracks. I echo other users in saying that, I would not want to use this for learning anything new. There is no point if I have to break my flow and verify after every query. But it is a good rubber duck. The chat interface can be used as a deprocrastination tool - when you are not able to come up with anything fruitful, just ask a few questions. This gets the ball rolling at least.
- grumpyprole 4y agoI don't understand why anyone is seriously expecting "GPT" to write code? Writing code is really analogous to proving theorems (the Curry-Howard correspondence). IIUC that is not what the GPT "language model" was ever designed for.
- deleted 4y ago[deleted]
- ZvG_Bonjwa 4y agoI ask this earnestly, but have you tried it? It most certainly can write a pretty huge variety of code. Yes, it does often have errors, and it struggles with novel or complex problems (as the article points out), but for well known languages and frameworks it is still insanely impressive.
- grumpyprole 4y agoI tried it yes, it did not go well. First it gave me a snippet that looked correct but was not what I was asking for (I guess similar to a search engine). When I tried to point out why the proposed solution did not solve the problem, it could not modify it correctly. It kept oscillating between two incorrect solutions.
- svantana 4y agoI don't think anyone expected LLMs to do this well on code problems. Just a few years ago they were struggling to get the syntax right. But since it does seem to work in many cases, it's only reasonable to explore its limits. But in the end, automated math & programming will most likely not be done using LLMs, but with purpose-built systems. The major players are of course already working on this, including OpenAI [1] and DeepMind [2]. [1] https://openai.com/research/formal-math https://openai.com/research/formal-math (feb '22) [2] https://www.deepmind.com/blog/competitive-programming-with-alphacode https://www.deepmind.com/blog/competitive-programming-with-a... (feb '22)
- rafaelero 4y agoI am skeptical this guy did a correct evaluation. I would like to see someone replicating this.
- ChicagoDave 4y agoThis has not been my experience. If you describe what you want, it gets very close to writing correct code. I’ve walked through a few very complex coding problems and although it took a few attempts, it eventually got there.
- drbig 4y agoHonest question: Why so many people attribute "thinking", "knowing, "understanding", "reasoning", "extrapolating" and even "symbolic reasoning" to the outputs of the advanced token-based probabilistic sequence generators, also known as LLMs? LLMs are inherently incapable of any that - as in mechanically incapable, in the same way a washing machine is incapable of being an airplane. Now my understanding is that the actual systems we have access to now have other components, with the LLM being the _core_, but not _sole_ component. Can anybody point me to any papers on those "auxiliary" systems? I would find it very interesting to see if there are any LLMs with logic components (e.g. Prolog-like facts database and basic rules that would enforce factual/numerical correctness; "The number of humans on Mars is zero." etc.).
- bsaul 4y agosome prompts results are only explainable if chatgpt has the ability to produce some kind of reasoning. As for your analogy, I'm not sure we know enough about human intelligence core mechanisms to be able to dismiss NN as being fundamentally incapable of it.
- mjburgess 4y agoThe reasoning occurred when people wrote the text it was trained on in the first place; it's training data is full of the symptoms of imagination, reason, intelligence, etc. Of course if you statistically sample from that in convincing ways it will convince you it has the properties of the systems (ie., people) which created its training data. But on careful inspection, it seems obvious it doesnt. Bugs bunny is funny because the writing staff were funny; bugs himself doesnt exist.
- SanderNL 4y agoIf you “sample” this enough to be reasoning in a general manner, what is exactly the problem here? Magic “reasoning fairy dust” missing from the formula? I get the argument and I think I agree. See Dreyfus and things like “the world is the model”. Thing is, the world could contain all intelligent patterns and we are just picking up on them. Composing them instead of creating them. This makes us automatons like AI, but who cares if the end result is the same?
- charlieyu1 4y agoNow I'm curious what kind of code GPT-4 produced here. I thought an LLM would be at least able to detect the suitable algorithm to use based on text analysis?
- adverbly 4y agoI find it interesting that we have a single word for creativity in the context of novel work. These systems are incredible at generating novel art or poetry. But when it comes to problem solving, they struggle adapting to a novel problem. With most humans, the ability to adapt to novelty seems to correlate with the ability to generate it. I have two theories on this. 1. Perhaps these are two very different things. Novel art is closer to having a high performing random number function. Adapting to novel situations is closer to mathematical or strategic thinking. 2. Perhaps we are deeply fooling ourselves by building the greatest Chinese room machine imaginable - and in reality this machine does not build general understanding the same way we do. Douglas Hofstadter would be very disappointed with us if this is the case. I still don't know which of these two seems more correct. Of course it might be both of them. I would like to see some more dedicated testing on the second point, as it does seem to be experimentally verifiable. For example, if this system truly has the ability to reason, it should be able to reason equally well in contexts where significant training data exists as well as contacts without any training data. We should be able to present the system with isomorphic problems across two different contexts and compare its performance to see if it has flat reasoning abilities.
- marcyb5st 4y agoA question to the adept in the filed. Isn't that expected? These models are trained by making them guess the next word given a sequence of words. This means that are not modeling causal relationships between the given context (the prompt) and the answer they output. And I believe for solving new problems making these kinds of causal connection is necessary. So, the fact they can actually do better than nothing does it mean that they have a very basic notion of causality or just that human predicting the next word from human content is a poor proxy for such kind of causal relationships?
- brocha 4y agoI never use ChatGPT for anything I don't already know how to do. The biggest thing I've had it writing for me recently though - unit tests. It writes them much quicker and even though I have to tweak them, the shell is done and allows me to focus on more important things after I verify and modify them.
- rybosworld 4y agoI'm surprised that so many people here seem to find this surprising. There's been a lot of "you aren't prompting it correctly" discourse. Some of it valid, some of it not. But there's also been many examples of people asking chatGPT to make minor modifications to a well-known problem and it completely flounders.
- auganov 4y agoThe insidious thing is "you aren't prompting it correctly" is kind of a truism. For every possible output there almost certainly is a prompt that produces it (at worst you can just tell it exactly what to output verbatim). The true believers can already do all their programming via ChatGPT regardless of whether or not there are real productivity gains. Not that different from all the other tools people claim turn them into 10x programmers while others remain unconvinced. So as long people enjoy the format, it's here to stay.
- rybosworld 4y agoIt's only a matter of time before chatGPT is trained on so many examples that it can provide a solution to 99.99% of coding prompts. I am curious to see how much that 0.01% will matter.
- getpost 4y agoThere will always be new programs that matter; they matter because they are new.
- vanattab 4y agoIn GPT-4 defense I do significantly worse(at least at first) on problems not in my training set.