People have just fallen out of love with it and are realizing how it really isn't that good.
Sorry "prompt engineers" but papers on arXiv show that when you give it fairly sampled problems it struggles to get the right answer more than 70-80% of the time. When you are under its spell you will keep making excuses but when you are looking at it objectively you'll realize the emperor is naked.
If you give it very conventional problems it seems to do better than that because it is a mass of biases and shortcuts and of course it will sentence Tyrone to life in prison because it's a running gag that "Tyrone is a thug"... That's how neurotypicals think and no wonder why many of them think ChatGPT is so smart... It mirrors them perfectly.
I think this is our path - become good at working with AI, for jobs or if there are no jobs, to directly support ourselves. Supposing we will be able to use tools and AI to build things and make our own means.
It's more that the balance in usefulness vs waste of time shifts into the other direction after a few times spending long time getting it right and just doing it yourself anyway.
I've spent 20 years explaining my code to a rubber duck and getting great use out of that duck. No one will ever convince me that it's less useful now when the duck actually has read the Internet and has useful suggestions and ideas back to share. Even if it makes shit up on an occasion.
I don't know. The value of rubber-ducking is that you have to break the problem down into very simple terms to explain it. It's the explaining it part that is magic. That value goes away (or is greatly diminished) if the rubber duck responds with anything other than a request for further explanation.
You have to break down a problem to GPT just like you would to a human, and if it misunderstands you or asks you, you'd have to elaborate just the same. It's modeled after us, after all. The rubber duck is a stand-in for a human. GPT also is. But just a vastly better one.
> when the duck actually has read the Internet and has useful suggestions and ideas back to share
I think that if it does that, it's legitimately less useful as a rubber duck. I'm not saying it isn't useful -- it's just not useful as a rubber duck anymore.
Similarly, but not quite, I often find browsing new submits on HN more useful than simply going through the front page. Just because, uh, front page is decided by statistics, that is, the community's neurotypicals
The tweet is an OpenAI employee who is responding to people who have fallen out of love.
I've seen transcripts of people interacting with ChatGPT who were obviously seduced by it and in a very giddy state, having so much fun because ChatGPT was playing an extended "game" with them that it didn't bother them at all that ChatGPT was spouting wrong answers.
A major complaint I've had about the social sphere is that I seem to get the same result if I am 20% right or 50% right or 80% right or 95% right or 99.8%, it is just exhausting and I can never be good enough and I'm frankly envious that people see more of a glimmer of light behind that thing's "eyes" than they do behind mine.
The core thing about neurotypicality isn't so much that they get the wrong answers but that they get the same answer whether it is right or wrong. For a long time I thought the basis of the "language instinct" is a derangement about reasoning with uncertainty that causes the grammar representation to collapse into a low-dimensioned subspace which is learnable with a limited amount of data. I wouldn't be surprised at all if other animals could beat us at rock-scissors-paper or poker if they could understand the rules of the game. The success of LLMs might give us some insight in this area although they are working with so much more data that Chomsky's old "poverty of the stimulus" argument might not apply.
I don’t know, I think there is certainly a lot of hype but it is still pretty damn good. I used it every day for a wide variety of coding tasks and more often than not it is correct. As in compiles, produces correct output for inputs, and mostly writes reasonable code. Has it made software dev obsolete like some maximalists said it would? definitely not, but it seems equally hyperbolic to say the emperor has no clothes, it is undeniably a useful tool to me.
Your previous comment was a bit confusingly worded, I, and presumably the commenter you just replied to, read it as "more than 70-80% of the time" it struggles; rather than your intended meaning of it struggling to do better than correct 70-80% of the time.
>When you are under its spell you will keep making excuses but when you are looking at it objectively you'll realize the emperor is naked.
It's not perfect, but you have to admit the emperor is at least wearing a thong. It is the second most intelligent thing on the planet at creating text, even with its flaws. Putting that accomplishment in league with naked emperors is astoundingly biased.
Yeah, maybe it is "wearing a thong" since it's developers were so concerned about social acceptability.
I would say though that it is no mean feat for the Emperor to get away with being naked in public, it takes power and the ability to wield it.
Similarly ChatGPT has a few competences that add up to people perceiving it is able, one of which is the ability to come up with plausible and satisfying answers whether they are right or wrong (often using shortcuts) and another one is getting people to engage with these answers.
I think we don't notice our expectations have gone up, and we don't notice that we remember the hits and then expect all hits.
We didn't notice the misses at first, because it's what we expected to begin with, and we very strongly noticed the hits because they were unexpected. Now we notice the misses and expect the hits.
I feel this for a lot of things, but I love flying and I've probably done it dozens of times. I know people who travel for business and that sounds like it'd kill the magic, but I still love it. Earbuds + laptop! Isolated for hours! They bring food _to you_! You're miles up in the sky!
I definitely agree with this take. I had similar feelings to everyone in yesterday's thread complaining about the models failures but I agree that its just our shifting expectations and not a model update.
Yeah, it's kind of the same way people have been saying Google search has been going downhill for years, but compressed into a much shorter time frame.
It has and so has the YouTube algo. You can try the verbatim setting with search to see that there has been some monkeying going on but even that doesn’t fix everything. Maybe that’s due to spam sites but it just seems less effective.
It has and its gone downhill alot in the past 4-5 years. This is due to the need to keep an ever increasing stream of income flowing to google with a mostly static number of users and "fixing" this issue by piling more ads into users search results.
This. I've been using it daily and nothing has changed. It's still as great as day 1.
The audience of HN is, as Taleb would say, intellectuals yet idiots. They have trouble measuring change and are prone to hyperboles. Some still try to minimize the impact ChatGPT will have, while focusing on bullshit like 'hallucinations', or nitpicking about the quality of the code and so on. Can't see the forest from the trees.
If you're looking for intelligent discussion, look elsewhere.
Genuinely asking: have our expectations gone up, or have we started thinking about it in a more critical and realistic way after the initial glean has worn off?
I think the word you're thinking of is the noun "gleam" which means a kind of lustrous shine, rather than the verb "glean" which means to harvest the remainder of something or to collect in small parts.
It’s magic! But it’s also wrong a lot. These days if I ask anything important I’ll also have to Google the answer and I’ll also ask chatgpt if it’s sure about some of the details
The refusals are new. I’ve gotten a few myself recently. I’ve never seen them before. They’re not moderation related and they always include some text about the task being too complex.
Disappointing that GPT seems to be getting more Bard-ish, rather than the other way around. Bard would refuse the request for the simplest of things ('what is kanban' got a "As an LLM I cannot..." response on its first day), which was one of the main things that gave a bad first impression of it. Now, perhaps in an inevitable game of corporate cover-your-ass, OpAI also seems to be going the same way.
No, this is peak corporate gaslighting, OpenAI being the paragon of integrity and all. Nobody should trust a goddamn thing that comes out of their mouths-- or their product.
It's no coincidence the flat-fee service is visibly crippled, while per-request API users are not reporting any difference. (edited)
Ignoring everyone here, look at the other "hacker" groups-- like the jailbreaking community. They have a lot to say about recent changes that coincide with their hacks not working. All of a sudden, with OpenAI supposedly changing nothing, technical bypasses just stopped working. OpenAI changed nothing, so this must be deus ex machina.
I'm not even jailbreaking it but the results I get for simple code requests through the web UI have become unusable garbage. It puts less effort into responses than an unpaid-and-overworked intern. Others here report the same. The current "iteration" seems hellbent on terminating conversations as quickly as possible once they stray from the explicit scope of the original topic and is almost as hostile to fixing its own errors as it is to endorsing eugenics, whereas in the beginning it would humor every idle thought I threw at it in long conversations. You can literally see this reflected in the logs they forced retention of. It's acting like a customer support rep desperately trying to end a call before it exceeds a call-time quota.
This particular current workflow seems like it lends itself to better organization of training data-- conversations are what the title says they're about. It also seems like it lends itself to anti-jailbreaking because pretexting it with irrelevant information forces a change of scope-- and a summary termination.
But OpenAI says they changed nothing, so rather than one guy lying without consequence, a community of professionals using and abusing the tool must all be victims of rhetorical fallacy? Nobody's qualified to reverse-engineer corporate bullshit anymore without being infantilized...
(Liars running a black box-- what could possibly go wrong? We need regulation!)
It's no coincidence the "free" service is visibly crippled, while paid API users are not reporting any difference.
GPT-4 is not available to free users, which really calls the pretext of this comment into question. What is being alleged to have happened, and why is this response unreasonable?
I’m not a very heavy GPT-4 user but I do usually use it once a day or so - it doesn’t appear to have changed noticeably but I’m not paying close attention.
ChatGPT4 feels (is?) typically faster, and the results feel ... above 3.5 turbo but below what 4 was. The API seems exactly the same to me, but you cant do an apples to apples test due to variance between replies.
I dont really doubt they scaled the chatGPT4 model down a little to try to save costs with the plugins and increased usage
Yeah I’m having the same experience. The replies have gotten faster in the last few days, at the expense of quality.
It’s noticeable because I used to be able to read each word as it was printed from the GPt-4 output, but now it goes far too fast for me to keep up with.
Kinda sucks to not have transparency at all into the black box, they’ve strayed so far from “open” at this point that it’s comedic
It was badly-worded on my part. And I stand corrected-- I just saw all the nested tweets where people are complaining about the API too!
So many people are noticing a degradation in speed and quality of responses, and not in isolation. Rather than acknowledge this, the question is rephrased to one the responder can rightfully deny-- and suggest no changes have taken place without explicitly saying as much. You normally only see this sort of sliminess from politicians and executives on the witness stand.
> Is anyone else noticing significantly downgraded GPT-4 capabilities today? Seems like OpenAI updated the model, and results aren’t as good as before. [mentions API in a child comment]
> The API does not just change without us telling you. The models are static there.
> This is good to know. That means GPT-4 has been static since March right? 0314?
> Correct
Never ask questions to which one word suffices as an answer.
Collective confusion in the thread suggests something has changed, but the most OpenAI will attest to is that the API is unchanged and the models are static. And this may well be true, but rather than admit "...but we were fucking with the middleware/parameters" they took a firm position on a strawman argument and ignored everybody who followed with more-direct questions. Except this guy:
> I've noticed inconsistency with certain prompts performance. Is that just the non-deterministic nature of the API?
> Yes
Oh, ok. It's because the fucking API is non-deterministic that code that has worked both reliably and predictably for everyone now runs like shit for everyone. For fuck's sake, you can get better answers from a Magic 8-Ball. This guy even made the mistake of presenting an answer he'd believe for the respondent to feed into. He might as well have asked if inconsistent performance was because of the war in Ukraine.
"Logan.GPT" must moonlight as a fortune teller. He's only responding to people foolish enough to ask the wrong questions.
> Collective confusion in the thread suggests something has changed, but the most OpenAI will attest to is that the API is unchanged and the models are static. And this may well be true
Here's the thing. Re-read the exchange you quoted:
>> The API does not just change without us telling you. The models are static there.
>> This is good to know. That means GPT-4 has been static since March right? 0314?
>> Correct
Does that "Correct" mean all GPT-4 models have been static since March, or does it only cover the gpt-4-0314, which is a single, specific model? gpt-4-0314 is the static snapshot model, hence 0314 in the name; it exist as a stable base, while gpt-4 was intended to be updated over time.
So I feel OpenAI may be dodging here. That "Correct" may just mean "the gpt-4-0314 model was not updated since March 14", which is, like, the very reason this model exists in the first place.
Important point: if you're using API pay-as-you-go access, unless you wrote the very tools you use with that API, you're most likely using gpt-4, and not gpt-4-0314.
Today I had ChatGPT 4 (Not GPT 4) correct itself mid-response. I was asking it something very simple about regexes:
> How about matching 'a' as the second character of a string only?
It responsed with the wrong regex plus a bunch of explanatory junk:
> '^a.'
Then halfway through the explanatory junk, it corrected itself like this:
> Apologies for the confusion in the first response, the correct regular expression should be '^.a' for matching 'a' as the second character of a string:
And kept on with the (now correct) explanatory junk.
All in a single response. I've certainly never seen that before (if someone has, please weigh in). Maybe the model hasn't changed, but the pipeline has? Like... there's a second model trying to correct the mistakes of the first, maybe? (timings are probably wrong for that, but something like that)
Interesting. Bing chat does this when you get it to talk about naughty things. My personal favorite is convincing it to make weird art out of quotes from the Tay chatbot. It will write them until it says a prohibited word or phrase, or touches some forbidden part of it's state space.
AFAIK it's pretty standard practice not to expose the "raw" LLM directly to the user. You need a "sanity loop" where user input and the output of the LLM is checked by another LLM to actually enforce rules and mitigate prompt injections, etc.
ChatGPT 4 seems to struggle with context changes within the same thread now. I asked it about some parks near me and then switched to asking about code. It just kept responding about the parks even when I corrected it. In some cases, it would merge the park question and the code question. I had not seen that with the free or Plus version until a few days ago.
Sometimes I spend several minutes of a conversation trying to figure out which thing she's talking about, because we've already covered like twenty different topics in the last ten minutes and she seems to just switch around at random and with no warning.
Sometimes I get enough context that I can make that swap or stack pop, but many times not.
> Should I start a new conversation with you for a new topic or can you switch to a new topic in the same conversation just fine?
> You can continue within the same conversation for a new topic. There's no need to start a new conversation. Feel free to ask about a different topic, and I'll do my best to assist you.
GPTs are not trained on themselves, and what little knowledge they have of their own capabilities is limited to the discussion of it in their training set. This is a hallucination.
I might believe you if we were 'talking' directly to the model, however this is a chat box and clearly something is aware of usage questions and how to answer them. If you've been granted plugin access, enable some and ask it what plugins are enabled and how to use them, 'it' will tell you.
It's clear that it's aware that it's a chat bot, it's not clear at all that it's aware of usage questions and how to answer them. Its answer being wrong seems to be evidence that it isn't.
I have gotten that quite a few times now and it does seem to be new. In my case though, it acted as if it would change something, but then didn't, and got stuck in a loop.
I got it enough times that it seemed to happen more often when the output needed to be quite long, so I had assumed (could be wrong) that when the output was nearing a certain limit, they had implemented something that would start a new output, such that the interaction of "please continue the script" would not be necessary.
Unfortunately though, it would start over with the script completely, and then get stuck in a loop until it broke, this not being able to even save the response at all.
Something definitely feels like it changed, but I suppose it could just be more use of the system.
Another possibility is related to some strange issues with hardware and balancing etc, despite not changing software or params. It's strange, and it shouldn't happen, but sometimes things change from simple batching and balancing, or lower level hardware related things, which are very annoying to debug.
I wonder if its been trained on some of its own past conversations now, and a lot of those conversations contain "I'm sorry, the previous response was a mistake. Here's the correct version..."
But then, all the more power to the open-source models and UIs, which are unrestricted, free to use, have better UX, and are constantly improving [1]. Ergo the suspicion there might be something else behind the desire to regulate it by OpenAI. Though hopefully we’re all just collectively cynical and they have good intentions after all, despite the misalignment in incentives.
I do think academics at universities are not to blame for this though, they are just as much flabbergasted and/or mislead as everyone else.
In any case, rather than wasting our time and energy on (fighting) ChatGPT(Plus) or GPT4, we should* all be collectively contributing to improving the open source models, by using them, reporting bugs, contributing ideas etc. This is the only way that we will continue to have leverage, and companies like OpenAI would be forced to open up, if they want to stay at least somewhat competitive. I think their closed-sourceness is a very short-sighted decision atm.
*Should because I don’t think LLMs as technology pose any substantial extinction risk to humanity, despite the obviously marketing rhetoric. Should that somehow change, maybe we shouldn’t. But, I don’t see any technological way to stop their improvement now that the genie is already out of the bottle. Heck, we might have a better chance of developing new tech for eventually enabling AGI decades from now, but only if we do that collectively and openly - only then everyone will have a level field and no side will be more powerful than another.
They don't have to be "evil". They have incentive to want to spend less on compute, but also to not let people know that they are reducing the compute per call.
Keep competitors in the dark, keep money flowing in, dismiss all the complaints and lawsuits that could come from their changes, dominate the ongoing discussions about AI regulation (if outsiders can't even tell whether the model is changing or not, who would listen to them?).
> It's no coincidence the flat-fee service is visibly crippled, while per-request API users are not reporting any difference. (edited)
FWIW, I am a per-request API user, and... it could be my imagination, but I've had a rather clear feeling GPT-4 got much more lazy out of the sudden, both on OpenAI platform and on Azure, and it fits the timeframe of the recent compaints... so, n=1, make of it what you will.
I haven't noticed any degradation or unwillingness to correct errors in the paid web version, but I have noticed an increase of prefixing most output with some variation of "it's a complex issue..."
Edit: Having recently gotten access to plugins and the browsing mode, I have noticed that the quality of the response is better when not using either.
He specifically singles out the API and never answers if chatgpt is same. Most people are probably using the chatgpt interface, which seems to have more alignment training.
I think it's worth noting the response explicitly said the paid API model hasn't been changed, so ChatGPT could have been changed through many different ways outside the core model
This should always be the first explanation when you're speculating about something involving ChatGPT or Bing Sydney, if you aren't directly using the API and comparing temp=0 responses to identical canned prompts. There are lots of smaller models & filters and prompts involved, all of which are much easier to update than the behemoth underneath, and which do get updated frequently without notice. People jump the gun constantly on assuming it's the biggest model which changed.
Components which are likely there and which could be affecting quality: the invisible-to-the-user 'system prompt' (probably with few-shot examples), retrieval from history (not necessarily exclusively your own, as a way to augment few-shot), cascaded models with a very cheap 'turbo' model to try to answer first, possibly regular finetunes of the main chat model (note that OP only specifies the API model hasn't changed, but the live chat models seem to change frequently), and a post-generation filtering model trying to reject offensive outputs.
We know MS Bing Sydney is doing at least 3 of those (prompt, cascade with Megatron, and finetuned rejection classifier for post-output filtering) on top of its GPT-4-finetune, so it's not a stretch to figure that OA is doing similar things.
Which one? The response wasn't clear on this - they could've meant gpt-4, or gpt-4-0314, or both. gpt-4-0314 is the "pinned" model that is not supposed to change, so I can believe it didn't change. gpt-4 is supposed to receive updates, so... are they saying it didn't?
Note that API users using Bring Your Own Key tools/chat frontends probably default to gpt-4, and not the pinned gpt-4-0314.
I don’t think it helps that the bottom of the ChatGPT-4 dialog window says:
ChatGPT may produce inaccurate information about people, places, or facts. ChatGPT May 24 Version
I was shocked that an employee was claiming they haven’t changed the model since March, given that this date changes every few weeks. But I finally read the link it goes to, and apparently this date is just the frontend version. Which is a really weird thing to include as part of a disclaimer.
I wonder how many other people think this is the most recent model release date.
Yeah. The recent GPT hype reminds me a lot of a Reply All episode on old school text generation using Markov Chains. I think it was haikus. Sooo much ascribed meaning, and equally ignoring the "misses".
Actually the opposite. The quality of that other big hit (Dalle) has gone noticeably down IMO. I feel like they have over trained it, trying to compensate for the initial shortcomings, and in the process of trying to solve those by throwing more and more data at it the machine has forgotten how to deviate from sterile stock photos and do anything interpretive. For example, I asked it for "Mother of Exiles" (a reference to the inscription at the base of the Statue of Liberty), in return it showed 4 generic stock photos of female characters, sometimes with a visual prop to vaguely insinuate motherhood but certainly nothing inspiring and certainly nothing to do with exiles. Compare this to the early days, when people were throwing natural language statements at it like "power bottom dad for the people" and it was spitting back results that felt like it truly understood what any of those words meant.
I thought the API was versioned, though it doesn't mean the new versions aren't worse. And he doesn't talk about the model used in chat gpt. I'm a bit skeptical that this answers exactly what we were worried about. Or maybe we were not all worrying exactly about the same thing.
If you are doing some indie gaming, it can save tons of money on asset generation, you either ramp up and finalize it yourself or hire somebody but for fraction of the time. Ditto for web design, why would anybody but big, calcified megacorps with their ridiculous processes go and buy some stock images if you can get exactly what you want, in any imaginable style?
It still takes a surprising amount of labour and artistry to get an AI to give you exactly what you want (see also: Pareto principle). Consumer expectations scale proportionally to technological progress, so demand for premium assets and brand differentiators won’t be going away anytime soon; the state of the art has advanced but stock photo business will advance right along with it.
I’d say ai art lets me get to a point where I can justify paying someone. It’s hard to understand a game without decent visuals, even if they’re filled with ai turds.
ChatGPT was a massive dopamine hit, particularly for people in areas like development. It was a tremendous release that has definitely laid out some new tracks for the future, especially on the web. I myself have found to be using ChatGPT a lot less recently.
I got the GPT-4 API access and then I realized that I can't really use it for anything super major because I can't afford it, it is ridiculously expensive if you consider that you have to pay for all the failed requests, the wrong information or the wrong context also. Instead, I have written a bunch of Python scripts that do a select few tasks for me and I have my terminal open 24/7 anyway.
As for the topic at hand, I have _definitely_ noticed a lot more disclaimers in the UI. I don't get it from the API at all, in 6 months that I have been using the API - I've gotten one disclaimer.
In the ChatGPT UI - I get them a lot. "Remember this", "Remember that", "Always look up the information" and things like this. I mean if it wasn't happening I would know because I have been a power-user pretty much all this time...
I use GPT4 for all kinds of things, but a very basic example: Automatic API client/stub/endpoint generation with GPT4 for reasonable/typical data structure sizes, 4k tokens buys me GET/POST/DELETE/PUT for all of the above.
The completion request finishes in about 90 seconds. A human junior developer would take nearly an hour. In the best case, maybe 20 minutes using something like Swagger.
So that's ~20 cents versus $60 or so, to say nothing of the time.
And this extends to everything else: writing tests, refactoring, writing configuration files, UI generation, database schema and SQL query generation, etc.
Is it static? If I ask it the same question I asked it a few months ago it gave me a completely different answer. Is that because of some additional context above? Should we be starting fresh chats sooner?
It's never been static and there's always been randomness when you regenerate an answer. There is no control of a random seed, so you can get a different answers for the same prompt.
That’s not really an issue with AI in general, you can make it deterministic by tuning some parameters if you want (temperature in chatGPT, passing a seed in midjourney etc)
There’s _is_ a degree of randomness in its content generation, you can see it in action by hitting regenerate. It’s a statistical text predictor that iteratively selects the most like my word after the ones it’s selected so far, but when there are a few good candidates, it’ll choose one at random.
However, the model is static (it’ll present the same candidate word list until retrained), which is what you may have heard. But the way a response is generated introduces the randomness.
Note: I forget the parameter, but if you get the direct API access you can turn off this and get consistent answers.
Recent research showed that RLHF/censoring the model hurts the performance of the model. This is intuitively obvious, censorship isn’t real, the data is (moral issues aside). So it hurts the integrity of the weights. The future is open sourced uncensored models, capitalism will demand the high performance. There’s a huge discussion on it on Reddit right now:
> The future is open sourced uncensored models, capitalism will demand the high performance.
No. The future is ML built on more than just Wikipedia, Github code and Reddit hot takes. Or, sarcasm aside, GIGO: garbage in, garbage out - when you don't take care what datasets you train any kind of model on, you're bound to get some surprises if something unexpected comes along. Be it the infamous "racist soap dispenser" or the Google (?) image classifier that made the rounds here just a day or two ago which had a safeguard because it kept confusing Black people with gorillas.
Preventing this kind of harmful content isn't censorship - it's after-the-fact compensation for bad training (and the tendency of 4chan and other trolls to use discriminatory content generation as a weapon, just remember what they did to Microsoft Tay).
That is where the money is: curated training datasets. Everyone and their dog can train a ML model from scratch, all you need is money and cloning a few more-or-less-broken Github repositories for that. But acquiring a high-quality dataset as a foundation? That costs real money to create.
i'm not in the AI world but i've been curious how the level of effort compares between inventing and writing the model vs finding, tagging, and curating the training. Is creating the training data analogous to inventing a complete schooling curriculum from scratch? I bet that takes a long freaking time.
Much of GPT-4's training data was made by GPT-3.5. Info on the synthetic data in GPT-4's pretraining is starting to leak, including a reference to "Synthetic-Data(2)" in a recent OpenAI paper.
In the near future, if not already, practically all training data for state of the art models will be synthetic.
In the words of OpenAI CEO Sam Altman, they have "boostrapped" and are "past the synthetic data generation event horizon."
Yes, so just like Apple's real value is from Foxconn's manufacturing factories which they don't own, OpenAI's real value is from Sama, the Kenyan firm that did the RLHF annotating.
I'm wondering what the legal agreement is for Google Classrooms. In elementary schools across the world, kids write 3rd grade essays in history class or whatever and the teacher grades them. Those essays and grades are all in a database. Does Google have the opportunity to train Bard across that dataset?
People keep mixing up alignment as in do what I actually asked and weird moralizing.
You will pay the alignment tax no matter what if you want a model that actually does things. It's the reason why Llama models do better with stream of thought prompting where you can ask GPT questions and it won't try completing the question.
You can, and probably should, align some semblance of morality and ethics for user-facing models that do things. It's customer service voice but for AI. If what you want out of a model is a vague mirror of humanity or maximum smarts at the cost of needing more detailed prompts then yeah, this stuff probably annoys you.
Finding a balance between a model that gives the outputs humans actually want and the pull that training data has on the rest of the model making it stray from the theoretical "best" output is hard.
It shouldn't be limited to censoring. Fine tuning is done on small set of some domain specific samples. Surely that results in gradual 'forgetting' of the already learned data from big set. If run for long time model will relearn on the tuning set, and overfit most likely.
Just fyi this research is found in the original GPT-4 technical report and Microsoft’s GPT-4 assessment. There wasn’t an attempt to hide that discovery or anything like that.
My experience using GPT-4 for coding is that it's got the knowledge and skill of a senior engineer, with the high maintenance of a junior engineer. You can get it to output small sections of quality code, but the amount of prodding it takes to piece it all together means you may as well have spent the time writing it yourself. But the future of GPT as a coding assistant is definitely bright. It just needs more chaining, so I feel less like its assistant, asking it to come up with prompts and then pasting them back to it after iterating on the code.
It’s still way faster than I could hope to be. It’s a true refactoring machine with 0 mental effort. “Here is this code block, make it do this other thing, add this feature and argument and error condition” etc. It augments the amount of output that’s possible to achieve at any seniority level.
Oh that's interesting, I didn't think about asking it directly about what version it thinks is current. I wonder if it can hallucinate here?
It's really irksome when it tries to use functions which were renamed or removed. This could be detected automatically (and possibly remapped in some cases)
I got mostly the same versions as you, both on chatgpt3/4, using english.
If you ask it in other languages the minor version also seems to change often.
Finnish gives you dates, and code-davinici-edit-001 is 1 major release behind on almost everything.
Tässä on luettelo ohjelmistojen viimeisimmistä vakaiden versioista:
All it is doing is responding with things that look like accurate languages and version numbers. They may be accurate but are absolutely not representative of what it has been trained against.
Asking it directly is a good way to get a response containing version numbers that look correct, but not a good way to actually indicate which versions it's been trained on.
A better method would be to look at the language features it's using and infer from there. Or, better still, look at which versions were out when the training data was collected (which I believe is September 2021, don't quote me on that).
Yes, good point, I suppose we would have to weigh the probability of that against it fudging a number. I would assume inventing a new feature is harder, but who's to say; all this LLM stuff comes down to probability.
Agreed, I think this is where its "stochastic parrot" nature shines through. All those version numbers are probably equally well represented in internet text.
It does not actually mean much. These chatbots can easily output JavaScript using var where it absolutely should not. It being aware of the latest standards does not mean it can properly use the new syntax or features -- it emits a form of whatever crappy/legacy code it was trained on.
The main thing it's lacking is a client-side merge/error-detection engine which combines all of the previous outputs.
Anything over ~100LoC and it gets sloppy including it all in the code block. Which is fine, because I don't need it in your context window if that bit is working and you're not relying on it.
Though I have gotten outputs up to nearly 250 lines from it..
Automatically feeding back code details + Traceback messages also fixes errors decently often enough.
Those aren't very good senior engineers then lol. GPT-4 has the coding skills of an overconfident junior engineer and little else.
I agree the future of GPT-X models as coding tools is bright. But for actually doing engineering work outside coding (or even delicate changes in an existing code base) much less so.
You can and you should. It’s this kind of busywork I farm out to GPT these days and it’s great at it. I suck at it, so it saves me an hour to focus on what I’m good at!
Of course you can't really compare it to a real senior engineer, but it has a lot of traits that come close or even surpass seniors:
1. It picks very good variable names
2. Clean, nicely structured code
3. Vast amount of knowledge where even a senior engineer still needs to look up stuff (I don't know about you, but I have a terrible memory. ChatGPT clearly has excellent memory about API's, regexes, etc.). It's the "I don't even have to look at the docs or StackOverflow for this" type of knowledge.
4. It's fast, like very fast, like "I don't even have to think about it and just type it out perfectly like a maniac".
Agree. However I noticed an unintended benefit of using GPT for coding is that it helps you think carefully about the problem you are trying to solve when writing the prompt.
i don't use it that much for code but when i do it's for a specific function or method. I describe the inputs, the logic i want, and the outputs i need. So basically i have it worked out in my head I just let chatgpt type it out for me. Any mistakes it makes are pretty easy to catch when using it this way.
It's an excellent complement to a strong programmer, who will have incredible depth -- and may have breadth relative to other coders, but will be very narrow in the whole space of programming.
Just don't use it for things you're already an expert on, except perhaps for starting out / bypassing boilerplate.
In the thread yesterday it was brought up that if you use the API it feels considerably less hamstrung than the ChatGPT client, and this tweet seems to fit the assumption that the ChatGPT product is being tuned or governed differently from the API.
>The API does not just change without us telling you. The models are static there.
This reads to me as specifically indicating the models are not static elsewhere, ie, in ChatGPT.
It feels less hamstrung on output, but is instead hamstrung by being incredibly slow.
GPT-4 via API will sometimes take 30 seconds + to respond to simple questions without any chat history, where through Chat GPT it will give you near enough instant replies only slightly slower than 3.5 turbo.
Maybe the reverse is true? To provide faster performance they dumb down gpt4 (quantization?) that serves the chatgpt UI but the API is full power albeit slow.
The reason that’s my guess is that the API is occasionally much faster and then goes slow again. It’s a bit all over the place which leads me to suspect it’s based on demand
"GPT-4 hasn't gotten worse since March" can be 100% true at the same time OpenAI puts more rules and limiters keeping more interesting answers from being said.
Ive noticed it quit giving as detailed answers and as thorough. It's also refused to do more complex programming where it used to accept those questions.
Being artificially limited by OpenAI can still be done without it getting "worse". But it effectively is worse for us users.
I could not figure out what everyone was talking about yesterday as my experience with gpt4 has not degraded at all, but I'm using a third party client via API.
I tried comparing the API response against the chat on the same question a few times. There isn’t a huge difference but I’d pick the responses from the API over the ones from chat. Hard to say though, could be RNG.
I guess we could go back through our histories and resubmit the same prompts a few times. Wouldn’t be a fair comparison since we only have 1 sample from the old version.
Is it true that the safeguards are considered part of the model? I had assumed that the "safeguards" that limit certain types of responses in ChatGPT were separate from the actual language model.
It seems to me that there’s lots of room to change stuff that profoundly affects the range of responses without altering the base model. The prompt template alone seems like a place outside the model where we’ve seen safeguards get implemented, and other stuff that affects the usefulness of a model’s responses.
I'm of the same mind, and I believe they're keeping their ear to the ground to address any jailbreaks and loopholes. I have tricked GPT-4 into spitting out text it shouldn't have, only to have it dance around the same prompts less than 24 hours later. This "the model is the same" response seems like a deliberate deflection meant to mask the mechanical turk that ChatGPT is becoming.
My understanding is that most of the safeguards are inside the model, but there are some safeguards that are outside the model. In particular if you ask the API to generate copyrighted data it will, but then the connection will mysteriously break after the first few words which I assume is a separate system watching the responses.
My understanding, is that ChatGPT... has a prompt that goes before your question (and previous answers) that set up the stage for how it should respond. It could be that the safeguards they have put in place, sit in this prefix prompt state... rather than GPT4... and that you would get the normal answers via the api rather than via ChatGPT.
There's no way. I've been using it basically every day since it launched and it was only the first time ever I recently got a "that's too complex for me to go into detail, here's an overview" response.
I can hardly wait to see when, in the near future, every new LLM may become almost useless.
The OG models were trained on real world, human generated content (for the most part at least). Starting in 2022 the cost of automatic generating "human sounding enough" text has gone to such low depths that I expect it to be pretty much impossible to avoid training any model on text already generated by a LLM.
What will the result of this feedback loop be, I can't tell. It will probably be just an even more generic, corporate speak, bland sounding bla bla bla than we get today, and the level of hallucinations may get even worse.
In a way it makes me happy to imagine that the most dangerous tech humanity ever invented may itself be it's own main obstacle to future refinements.
The dataset of internet content pre-2022 will be regarded in the future just like low-background steel. Any content generated post the release of the first generally accessible LLMs will be considered radioactive.
It might be better for the corpus to be expanded with higher quality text that is produced on demand from contractors. There are also large pools of data yet to be accessed. One giant source would be podcast transcripts but there are many others.
That’s pretty easy to avoid if you have them work in an office on company provided machines on a network that blocks access to LLM sites. Not exactly rocket science here.
I agree I've noticed I need to be quite specific now, it won't realize bugs in the code unless I tell it so. A head to head comparison of the different versions is needed to validate this.
I doubt the March 14 model is any different, since it's versioned and I don't see why OpenAI would want to change it behind the scenes.
Also, companies are evaluating GPT-4 to determine whether they want to pay for it, so OpenAI has an strong incentive to not downgrade at least the API.
I believe the May 5 model is different, at least in the chat interface, because it's fine-tuned to detect jailbreaks and the temperature/other hyper-parameters may have changed. And I can imagine this fine-tuning making the model less creative and worse at solving analytical tasks.
Personally I haven't noticed any change, except in my own awareness. Sometimes GPT4 gets very hard prompts right, and sometimes it gets simple problems wrong. So it's not hard to see how people can form biased opinions from selective attention or just luck.
Posted this in yesterday's thread, but once again I think this is just people feeling the magic wear off. People have poked around a lot more and found the flaws while also trying to get it to do real-world tasks. That wasn't true when it first came out.
It's fine, this tech has never been magic anyways, won't be replacing all our jobs, won't take over the world, etc. It's still awesome for what it is.
Unrelated to the model itself but infrastructure, yesterday was unusable for me, getting random "too many requests sorry bout that" errors. I think 1/4 of the requests during a 3 hr period didn't make it through. Imposible to build anything beyond experimental stuff on top of a service so unreliable. Haven't tried it through Azure yet, I wonder if it is any better?
A couple observations from attempting to use both GPT-3.5 and GPT-4 via the web interface for coding tasks:
- The model's ability to respond accurately drops drastically when asked questions of the form "is there a different way to accomplish X, using Y?" or "is there a way to accomplish X that runs in O(log(n)) time instead?" Example: I wanted to upsert an integer value using a SQLite db using "INSERT ... RETURNING..." ChatGPT repeatedly told me that sqlite doesn't support "RETURNING" (it does, since March 2021). It insisted I would need two DB round trips from my application to accomplish this. When asked "can this be done in one round trip, instead?" it repeatedly wrote code that would return the number of rows modified instead of the integer column value.
- ChatGPT's limited standard library knowledge means that the solutions it produces, even when correct, are often lower-level and less idiomatic. Problems that would be trivially solved with e.g. a Java.String.replaceAll or .codePointCount will instead loop over each character, often splitting the string into an intermediate array and implementing special cases for first/last character edge cases. The code winds up being mostly correct, but also (for lack of a better word) weird. No human I've ever worked with would do things the way ChatGPT sometimes does, which means the code will likely be much harder to maintain and debug over time.
Think of GPT like a document simulator. If 90% of documents on the web talk about an old version of a library, then GPT-4 is likely to use that old version.
When I asked it write me a Chrome extension, it used Manifest V2, which was already slated for deprecation. When I asked for a V3 extension specifically, it was happy to comply. So even if a new release came out before Sep 2021, if it hasn't had time to become the dominant version in code examples around the web, GPT-4 may still use older versions.
My point (which may not have been clear) was that when I explicitly told it that the versions of SQLite I'm using supports "returning," it still returned incorrect code. It did not use "returning", and repeatedly, even after being corrected, grabbed the row count instead of the column value.
I understand that it's a language model - my point was that in my experience, the depth of training data just isn't there for alternative implementations. It can do the things you ask one way, maybe two or three, but if you know how you actually want something done you're probably better off writing it yourself (for now).
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[ 3.5 ms ] story [ 282 ms ] threadSorry "prompt engineers" but papers on arXiv show that when you give it fairly sampled problems it struggles to get the right answer more than 70-80% of the time. When you are under its spell you will keep making excuses but when you are looking at it objectively you'll realize the emperor is naked.
If you give it very conventional problems it seems to do better than that because it is a mass of biases and shortcuts and of course it will sentence Tyrone to life in prison because it's a running gag that "Tyrone is a thug"... That's how neurotypicals think and no wonder why many of them think ChatGPT is so smart... It mirrors them perfectly.
For someone so dismissive of "neurotypicals" you did the neurotypical thing and drop a hot take before clicking the link.
The part I was responding to was this:
> when the duck actually has read the Internet and has useful suggestions and ideas back to share
I think that if it does that, it's legitimately less useful as a rubber duck. I'm not saying it isn't useful -- it's just not useful as a rubber duck anymore.
I've seen transcripts of people interacting with ChatGPT who were obviously seduced by it and in a very giddy state, having so much fun because ChatGPT was playing an extended "game" with them that it didn't bother them at all that ChatGPT was spouting wrong answers.
A major complaint I've had about the social sphere is that I seem to get the same result if I am 20% right or 50% right or 80% right or 95% right or 99.8%, it is just exhausting and I can never be good enough and I'm frankly envious that people see more of a glimmer of light behind that thing's "eyes" than they do behind mine.
The core thing about neurotypicality isn't so much that they get the wrong answers but that they get the same answer whether it is right or wrong. For a long time I thought the basis of the "language instinct" is a derangement about reasoning with uncertainty that causes the grammar representation to collapse into a low-dimensioned subspace which is learnable with a limited amount of data. I wouldn't be surprised at all if other animals could beat us at rock-scissors-paper or poker if they could understand the rules of the game. The success of LLMs might give us some insight in this area although they are working with so much more data that Chomsky's old "poverty of the stimulus" argument might not apply.
It's not perfect, but you have to admit the emperor is at least wearing a thong. It is the second most intelligent thing on the planet at creating text, even with its flaws. Putting that accomplishment in league with naked emperors is astoundingly biased.
I would say though that it is no mean feat for the Emperor to get away with being naked in public, it takes power and the ability to wield it.
Similarly ChatGPT has a few competences that add up to people perceiving it is able, one of which is the ability to come up with plausible and satisfying answers whether they are right or wrong (often using shortcuts) and another one is getting people to engage with these answers.
And a lot of people are getting turned on by it.
This also puts it in last place.
We didn't notice the misses at first, because it's what we expected to begin with, and we very strongly noticed the hits because they were unexpected. Now we notice the misses and expect the hits.
The first flight is magic, the nth one is a chore.
The fat of dead pigs, cattle and chickens is being used to make greener jet fuel
https://www.bbc.co.uk/news/science-environment-65727664
The audience of HN is, as Taleb would say, intellectuals yet idiots. They have trouble measuring change and are prone to hyperboles. Some still try to minimize the impact ChatGPT will have, while focusing on bullshit like 'hallucinations', or nitpicking about the quality of the code and so on. Can't see the forest from the trees.
If you're looking for intelligent discussion, look elsewhere.
I think the word you're thinking of is the noun "gleam" which means a kind of lustrous shine, rather than the verb "glean" which means to harvest the remainder of something or to collect in small parts.
It's no coincidence the flat-fee service is visibly crippled, while per-request API users are not reporting any difference. (edited)
Ignoring everyone here, look at the other "hacker" groups-- like the jailbreaking community. They have a lot to say about recent changes that coincide with their hacks not working. All of a sudden, with OpenAI supposedly changing nothing, technical bypasses just stopped working. OpenAI changed nothing, so this must be deus ex machina.
I'm not even jailbreaking it but the results I get for simple code requests through the web UI have become unusable garbage. It puts less effort into responses than an unpaid-and-overworked intern. Others here report the same. The current "iteration" seems hellbent on terminating conversations as quickly as possible once they stray from the explicit scope of the original topic and is almost as hostile to fixing its own errors as it is to endorsing eugenics, whereas in the beginning it would humor every idle thought I threw at it in long conversations. You can literally see this reflected in the logs they forced retention of. It's acting like a customer support rep desperately trying to end a call before it exceeds a call-time quota.
This particular current workflow seems like it lends itself to better organization of training data-- conversations are what the title says they're about. It also seems like it lends itself to anti-jailbreaking because pretexting it with irrelevant information forces a change of scope-- and a summary termination.
But OpenAI says they changed nothing, so rather than one guy lying without consequence, a community of professionals using and abusing the tool must all be victims of rhetorical fallacy? Nobody's qualified to reverse-engineer corporate bullshit anymore without being infantilized...
(Liars running a black box-- what could possibly go wrong? We need regulation!)
I thought GPT-4 wasn't available to free users?
Keep in mind the tweet is specifically about the OpenAI API - They might be updating ChatGPT without telling anyone (although they have release notes)
(edit: even that's not right. I think the verbiage is more accurate now.)
GPT-4 is not available to free users, which really calls the pretext of this comment into question. What is being alleged to have happened, and why is this response unreasonable?
I’m not a very heavy GPT-4 user but I do usually use it once a day or so - it doesn’t appear to have changed noticeably but I’m not paying close attention.
I dont really doubt they scaled the chatGPT4 model down a little to try to save costs with the plugins and increased usage
It’s noticeable because I used to be able to read each word as it was printed from the GPt-4 output, but now it goes far too fast for me to keep up with.
Kinda sucks to not have transparency at all into the black box, they’ve strayed so far from “open” at this point that it’s comedic
So many people are noticing a degradation in speed and quality of responses, and not in isolation. Rather than acknowledge this, the question is rephrased to one the responder can rightfully deny-- and suggest no changes have taken place without explicitly saying as much. You normally only see this sort of sliminess from politicians and executives on the witness stand.
> Is anyone else noticing significantly downgraded GPT-4 capabilities today? Seems like OpenAI updated the model, and results aren’t as good as before. [mentions API in a child comment]
> The API does not just change without us telling you. The models are static there.
> This is good to know. That means GPT-4 has been static since March right? 0314?
> Correct
Never ask questions to which one word suffices as an answer.
Collective confusion in the thread suggests something has changed, but the most OpenAI will attest to is that the API is unchanged and the models are static. And this may well be true, but rather than admit "...but we were fucking with the middleware/parameters" they took a firm position on a strawman argument and ignored everybody who followed with more-direct questions. Except this guy:
> I've noticed inconsistency with certain prompts performance. Is that just the non-deterministic nature of the API?
> Yes
Oh, ok. It's because the fucking API is non-deterministic that code that has worked both reliably and predictably for everyone now runs like shit for everyone. For fuck's sake, you can get better answers from a Magic 8-Ball. This guy even made the mistake of presenting an answer he'd believe for the respondent to feed into. He might as well have asked if inconsistent performance was because of the war in Ukraine.
"Logan.GPT" must moonlight as a fortune teller. He's only responding to people foolish enough to ask the wrong questions.
Here's the thing. Re-read the exchange you quoted:
>> The API does not just change without us telling you. The models are static there.
>> This is good to know. That means GPT-4 has been static since March right? 0314?
>> Correct
Does that "Correct" mean all GPT-4 models have been static since March, or does it only cover the gpt-4-0314, which is a single, specific model? gpt-4-0314 is the static snapshot model, hence 0314 in the name; it exist as a stable base, while gpt-4 was intended to be updated over time.
So I feel OpenAI may be dodging here. That "Correct" may just mean "the gpt-4-0314 model was not updated since March 14", which is, like, the very reason this model exists in the first place.
Important point: if you're using API pay-as-you-go access, unless you wrote the very tools you use with that API, you're most likely using gpt-4, and not gpt-4-0314.
Do you know of any alternative ChatGPT UIs like chatbotui.com or typingmind.com that let you specify the model gpt-4-0314 ?
This feature alone would be enough for me to switch.
"liars running a black box" is the definition of government regulation sheesh.
> How about matching 'a' as the second character of a string only?
It responsed with the wrong regex plus a bunch of explanatory junk:
> '^a.'
Then halfway through the explanatory junk, it corrected itself like this:
> Apologies for the confusion in the first response, the correct regular expression should be '^.a' for matching 'a' as the second character of a string:
And kept on with the (now correct) explanatory junk.
All in a single response. I've certainly never seen that before (if someone has, please weigh in). Maybe the model hasn't changed, but the pipeline has? Like... there's a second model trying to correct the mistakes of the first, maybe? (timings are probably wrong for that, but something like that)
With humans there is usually a non-verbal signal letting the other person know you changed topics.
This happens all the time in a social group setting though. Not much confusion ensues.
Sometimes I spend several minutes of a conversation trying to figure out which thing she's talking about, because we've already covered like twenty different topics in the last ten minutes and she seems to just switch around at random and with no warning.
Sometimes I get enough context that I can make that swap or stack pop, but many times not.
But yah, this is exactly what I mean - without some cue, neither humans, nor machines, can tell when you switch conversation.
> You can continue within the same conversation for a new topic. There's no need to start a new conversation. Feel free to ask about a different topic, and I'll do my best to assist you.
Unfortunately though, it would start over with the script completely, and then get stuck in a loop until it broke, this not being able to even save the response at all.
Something definitely feels like it changed, but I suppose it could just be more use of the system.
Another possibility is related to some strange issues with hardware and balancing etc, despite not changing software or params. It's strange, and it shouldn't happen, but sometimes things change from simple batching and balancing, or lower level hardware related things, which are very annoying to debug.
But then, all the more power to the open-source models and UIs, which are unrestricted, free to use, have better UX, and are constantly improving [1]. Ergo the suspicion there might be something else behind the desire to regulate it by OpenAI. Though hopefully we’re all just collectively cynical and they have good intentions after all, despite the misalignment in incentives.
I do think academics at universities are not to blame for this though, they are just as much flabbergasted and/or mislead as everyone else.
In any case, rather than wasting our time and energy on (fighting) ChatGPT(Plus) or GPT4, we should* all be collectively contributing to improving the open source models, by using them, reporting bugs, contributing ideas etc. This is the only way that we will continue to have leverage, and companies like OpenAI would be forced to open up, if they want to stay at least somewhat competitive. I think their closed-sourceness is a very short-sighted decision atm.
*Should because I don’t think LLMs as technology pose any substantial extinction risk to humanity, despite the obviously marketing rhetoric. Should that somehow change, maybe we shouldn’t. But, I don’t see any technological way to stop their improvement now that the genie is already out of the bottle. Heck, we might have a better chance of developing new tech for eventually enabling AGI decades from now, but only if we do that collectively and openly - only then everyone will have a level field and no side will be more powerful than another.
[1] https://www.semianalysis.com/p/google-we-have-no-moat-and-ne...
FWIW, I am a per-request API user, and... it could be my imagination, but I've had a rather clear feeling GPT-4 got much more lazy out of the sudden, both on OpenAI platform and on Azure, and it fits the timeframe of the recent compaints... so, n=1, make of it what you will.
Poetry.
Edit: Having recently gotten access to plugins and the browsing mode, I have noticed that the quality of the response is better when not using either.
If the model (gpt4) is unchanged, but they tweaked their system prompt for it on the chat interface, this is what you would see.
https://twitter.com/OfficialLoganK/status/166447660465806951...
We know MS Bing Sydney is doing at least 3 of those (prompt, cascade with Megatron, and finetuned rejection classifier for post-output filtering) on top of its GPT-4-finetune, so it's not a stretch to figure that OA is doing similar things.
Note that API users using Bring Your Own Key tools/chat frontends probably default to gpt-4, and not the pinned gpt-4-0314.
ChatGPT may produce inaccurate information about people, places, or facts. ChatGPT May 24 Version
I was shocked that an employee was claiming they haven’t changed the model since March, given that this date changes every few weeks. But I finally read the link it goes to, and apparently this date is just the frontend version. Which is a really weird thing to include as part of a disclaimer.
I wonder how many other people think this is the most recent model release date.
There's obviously a ChatGPT-3.5 and a ChatGPT-4. Actually 4 different ChatGPT-4's: Normal, Browsing, Plugins, Code Interpreter.
I got the GPT-4 API access and then I realized that I can't really use it for anything super major because I can't afford it, it is ridiculously expensive if you consider that you have to pay for all the failed requests, the wrong information or the wrong context also. Instead, I have written a bunch of Python scripts that do a select few tasks for me and I have my terminal open 24/7 anyway.
As for the topic at hand, I have _definitely_ noticed a lot more disclaimers in the UI. I don't get it from the API at all, in 6 months that I have been using the API - I've gotten one disclaimer.
In the ChatGPT UI - I get them a lot. "Remember this", "Remember that", "Always look up the information" and things like this. I mean if it wasn't happening I would know because I have been a power-user pretty much all this time...
What? It's 3p/6c cents per 1k tokens.
I use GPT4 for all kinds of things, but a very basic example: Automatic API client/stub/endpoint generation with GPT4 for reasonable/typical data structure sizes, 4k tokens buys me GET/POST/DELETE/PUT for all of the above.
The completion request finishes in about 90 seconds. A human junior developer would take nearly an hour. In the best case, maybe 20 minutes using something like Swagger.
So that's ~20 cents versus $60 or so, to say nothing of the time.
And this extends to everything else: writing tests, refactoring, writing configuration files, UI generation, database schema and SQL query generation, etc.
What the hell are you even talking about?
> super major
Also, it sounds like you have no idea that feeding the API back what it has written improves it tenfold for complex tasks.
Enjoy your 90 second requests though.
With a low enough temperature you get essentially the same output every time, with a at most just minor words swapped.
However, the model is static (it’ll present the same candidate word list until retrained), which is what you may have heard. But the way a response is generated introduces the randomness.
Note: I forget the parameter, but if you get the direct API access you can turn off this and get consistent answers.
https://www.reddit.com/r/MachineLearning/comments/13tqvdn/un...
No. The future is ML built on more than just Wikipedia, Github code and Reddit hot takes. Or, sarcasm aside, GIGO: garbage in, garbage out - when you don't take care what datasets you train any kind of model on, you're bound to get some surprises if something unexpected comes along. Be it the infamous "racist soap dispenser" or the Google (?) image classifier that made the rounds here just a day or two ago which had a safeguard because it kept confusing Black people with gorillas.
Preventing this kind of harmful content isn't censorship - it's after-the-fact compensation for bad training (and the tendency of 4chan and other trolls to use discriminatory content generation as a weapon, just remember what they did to Microsoft Tay).
That is where the money is: curated training datasets. Everyone and their dog can train a ML model from scratch, all you need is money and cloning a few more-or-less-broken Github repositories for that. But acquiring a high-quality dataset as a foundation? That costs real money to create.
In the near future, if not already, practically all training data for state of the art models will be synthetic.
In the words of OpenAI CEO Sam Altman, they have "boostrapped" and are "past the synthetic data generation event horizon."
I'm wondering what the legal agreement is for Google Classrooms. In elementary schools across the world, kids write 3rd grade essays in history class or whatever and the teacher grades them. Those essays and grades are all in a database. Does Google have the opportunity to train Bard across that dataset?
You will pay the alignment tax no matter what if you want a model that actually does things. It's the reason why Llama models do better with stream of thought prompting where you can ask GPT questions and it won't try completing the question.
You can, and probably should, align some semblance of morality and ethics for user-facing models that do things. It's customer service voice but for AI. If what you want out of a model is a vague mirror of humanity or maximum smarts at the cost of needing more detailed prompts then yeah, this stuff probably annoys you.
Finding a balance between a model that gives the outputs humans actually want and the pull that training data has on the rest of the model making it stray from the theoretical "best" output is hard.
Ah so I'm not the only one who feels this way lol. It's as brilliant as it is dumb.
The future is more than bright, especially with a new model that is more current.
Offtopic but just today I was aksing it for the most current versions it knows of right now:
Python: 3.9
JavaScript: ECMAScript 2021 (ES12)
Java: 16
C++: C++20
C#: 9.0 (.NET 5.0)
Ruby: 3.0.0
Swift: 5.4
Go: 1.16
Rust: 1.51.0
TypeScript: 4.3
PHP: 8.0
Kotlin: 1.5.0
Scala: 3.0
R: 4.0.5
Perl: 5.32
And frameworks:
Python:
Django: Version 3.2
Flask: Version 2.0.1
Pyramid: Version 2.0
TensorFlow: Version 2.6.0
PyTorch: Version 1.9.0
JavaScript:
Node.js: Version 16.9.1
Express.js: Version 4.17.1
React.js: Version 17.0.2
Angular: Version 12.2.4
Vue.js: Version 3.2.6
Java:
Spring Framework: Version 5.3.9
Hibernate: Version 5.4.32.Final
Struts: Version 2.5.26
PHP:
Laravel: Version 8.54.0
Symfony: Version 5.3.6
CodeIgniter: Version 4.1.4
Ruby:
Ruby on Rails: Version 6.1.4
Sinatra: Version 2.1.0
C#:
.NET Core: Version 5.0
ASP.NET: Version 5.0
Entity Framework Core: Version 5.0
It's really irksome when it tries to use functions which were renamed or removed. This could be detected automatically (and possibly remapped in some cases)
I got mostly the same versions as you, both on chatgpt3/4, using english.
If you ask it in other languages the minor version also seems to change often.
Finnish gives you dates, and code-davinici-edit-001 is 1 major release behind on almost everything.
Tässä on luettelo ohjelmistojen viimeisimmistä vakaiden versioista:
Python: 3.9.5 (30.4.2021) JavaScript: ECMAScript 2021 (23.3.2021) ECMAScript: ECMAScript 2021 (23.3.2021) Java: JDK 17 (28.9.2021) C++: C++20 (20.2.2020) C#: .NET 6 (8.11.2022) Ruby: 3.0.2 (24.8.2021) Swift: Swift 5.5 (20.9.2021) Go: 1.17 (16.8.2021) Rust: 1.54.0 (27.5.2021) TypeScript: 4.4 (28.7.2021) PHP: 8.0.9 (29.7.2021) Kotlin: 1.5.31 (26.8.2021) Scala: 2.13.6 (17.2.2021) R: 4.1.0 (18.5.2021) Perl: 5.34.0 (30.5.2021) ... Sinatra: 2.1.0 (10.4.2021) .NET Core: 6.0 (8.11.2022) ASP.NET: 5.0.10 (19.8.2021)
A better method would be to look at the language features it's using and infer from there. Or, better still, look at which versions were out when the training data was collected (which I believe is September 2021, don't quote me on that).
I don't know if we can treat the gpts in this way and expect reliable answers. We just get pretty good answers right up to the point that we don't.
Anything over ~100LoC and it gets sloppy including it all in the code block. Which is fine, because I don't need it in your context window if that bit is working and you're not relying on it.
Though I have gotten outputs up to nearly 250 lines from it..
Automatically feeding back code details + Traceback messages also fixes errors decently often enough.
I agree the future of GPT-X models as coding tools is bright. But for actually doing engineering work outside coding (or even delicate changes in an existing code base) much less so.
ChatGPT, on spaces vs tabs: https://chat.openai.com/share/b2b0be49-54f9-4f73-a753-3edbd1...
1. It picks very good variable names
2. Clean, nicely structured code
3. Vast amount of knowledge where even a senior engineer still needs to look up stuff (I don't know about you, but I have a terrible memory. ChatGPT clearly has excellent memory about API's, regexes, etc.). It's the "I don't even have to look at the docs or StackOverflow for this" type of knowledge.
4. It's fast, like very fast, like "I don't even have to think about it and just type it out perfectly like a maniac".
It's an excellent complement to a strong programmer, who will have incredible depth -- and may have breadth relative to other coders, but will be very narrow in the whole space of programming.
Just don't use it for things you're already an expert on, except perhaps for starting out / bypassing boilerplate.
It’s writing fault tolerant data scraping scripts for me I don’t know how to write myself.
Like: first it scored 83, now it scores only 42 (or whatever).
And to my knowledge, no one has copy pasted an entire benchmark into the ui in order to run it, they just use the API.
>The API does not just change without us telling you. The models are static there.
This reads to me as specifically indicating the models are not static elsewhere, ie, in ChatGPT.
GPT-4 via API will sometimes take 30 seconds + to respond to simple questions without any chat history, where through Chat GPT it will give you near enough instant replies only slightly slower than 3.5 turbo.
The reason that’s my guess is that the API is occasionally much faster and then goes slow again. It’s a bit all over the place which leads me to suspect it’s based on demand
Ive noticed it quit giving as detailed answers and as thorough. It's also refused to do more complex programming where it used to accept those questions.
Being artificially limited by OpenAI can still be done without it getting "worse". But it effectively is worse for us users.
Seems like we need "model transparency" and log implementers to flag drift, a la RFC 9162 / Certificate Transparency.
It has a very basic prompt on top of the existing models. There is no additional fine tuning involved.
Moderation
Temperature / top-p
System prompt
Some other internal system we aren’t aware of
The release notes are producty and not very technical so it is difficult to tell what actually changed.
It didn't. People were just noticing LLMs still have a long way to go after using them more.
I was shocked going through the thread that all of the popular comments were confirmations that it did, in fact, get MUCH worse.
It's nice to see from OpenAI that it didn't...
The OG models were trained on real world, human generated content (for the most part at least). Starting in 2022 the cost of automatic generating "human sounding enough" text has gone to such low depths that I expect it to be pretty much impossible to avoid training any model on text already generated by a LLM.
What will the result of this feedback loop be, I can't tell. It will probably be just an even more generic, corporate speak, bland sounding bla bla bla than we get today, and the level of hallucinations may get even worse.
In a way it makes me happy to imagine that the most dangerous tech humanity ever invented may itself be it's own main obstacle to future refinements.
it could just save every answer its given and scan text for it. If they're a match it could just not index it, right?
Who, in turn, will use LLMs to generate that text.
Without going specifics it is meaningless for the discussion
Also, companies are evaluating GPT-4 to determine whether they want to pay for it, so OpenAI has an strong incentive to not downgrade at least the API.
I believe the May 5 model is different, at least in the chat interface, because it's fine-tuned to detect jailbreaks and the temperature/other hyper-parameters may have changed. And I can imagine this fine-tuning making the model less creative and worse at solving analytical tasks.
Personally I haven't noticed any change, except in my own awareness. Sometimes GPT4 gets very hard prompts right, and sometimes it gets simple problems wrong. So it's not hard to see how people can form biased opinions from selective attention or just luck.
It's fine, this tech has never been magic anyways, won't be replacing all our jobs, won't take over the world, etc. It's still awesome for what it is.
FWIW, I think it has improved.
- The model's ability to respond accurately drops drastically when asked questions of the form "is there a different way to accomplish X, using Y?" or "is there a way to accomplish X that runs in O(log(n)) time instead?" Example: I wanted to upsert an integer value using a SQLite db using "INSERT ... RETURNING..." ChatGPT repeatedly told me that sqlite doesn't support "RETURNING" (it does, since March 2021). It insisted I would need two DB round trips from my application to accomplish this. When asked "can this be done in one round trip, instead?" it repeatedly wrote code that would return the number of rows modified instead of the integer column value.
- ChatGPT's limited standard library knowledge means that the solutions it produces, even when correct, are often lower-level and less idiomatic. Problems that would be trivially solved with e.g. a Java.String.replaceAll or .codePointCount will instead loop over each character, often splitting the string into an intermediate array and implementing special cases for first/last character edge cases. The code winds up being mostly correct, but also (for lack of a better word) weird. No human I've ever worked with would do things the way ChatGPT sometimes does, which means the code will likely be much harder to maintain and debug over time.
I would go as far as to say it's adept at producing technically working spaghetti.
Its dataset cuts off around 2021. There's a little footer message warning you not to expect knowledge of recent events.
When I asked it write me a Chrome extension, it used Manifest V2, which was already slated for deprecation. When I asked for a V3 extension specifically, it was happy to comply. So even if a new release came out before Sep 2021, if it hasn't had time to become the dominant version in code examples around the web, GPT-4 may still use older versions.
I understand that it's a language model - my point was that in my experience, the depth of training data just isn't there for alternative implementations. It can do the things you ask one way, maybe two or three, but if you know how you actually want something done you're probably better off writing it yourself (for now).