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edit: I take it back. This is terrible, this is actually worse than anecdotal since it's basically a terrible representation of an existing paper.
The linked twitter account is an AI influencer, so take whatever is written with a grain of salt. Their goal is to get clicks and views by saying controversial things. This topic has come up before, and my hypothesis is still that GPT-4 hasn't gotten worse, it's just that the magic has worn off as we've used this tech. Studies to evaluate it have gotten better and cleaned up mistakes in the past.
So the original paper from Stanford and Berkley is also linked to this AI influencer? I am really amazed by this kind of dismissal. Its totally irrelevant who posted the info and how framed it is, as long as you have access to the source.
https://news.ycombinator.com/item?id=36781968
EDIT: FWIW I haven't noticed any such regression. I don't generally use it to find prime numbers, but I do use it for coding, and have been really impressed with what it's able to do.
8This paper is being misinterpreted. The degradations reported are somewhat peculiar to the authors' task selection and evaluation method and can easily result from fine tuning rather than intentionally degrading GPT-4's performance for cost saving reasons.
They report 2 degradations: code generation & math problems. In both cases, they report a behavior change (likely fine tuning) rather than a capability decrease (possibly intentional degradation). The paper confuses these a bit: they mostly say behavior, including in the title, but the intro says capability in a couple of places.
Code generation: the change they report is that the newer GPT-4 adds non-code text to its output. They don't evaluate the correctness of the code. They merely check if the code is directly executable. So the newer model's attempt to be more helpful counted against it.
Math problems (primality checking): to solve this the model needs to do chain of thought. For some weird reason, the newer model doesn't seem to do so when asked to think step by step (but the current ChatGPT-4 does, as you can easily check). The paper doesn't say that the accuracy is worse conditional on doing CoT.
The other two tasks are visual reasoning and answering sensitive questions. On the former, they report a slight improvement. On the latter, they report that the filters are much more effective — unsurprising since we know that OpenAI has been heavily tweaking these.
In short, everything in the paper is consistent with fine tuning. It is possible that OpenAI is gaslighting everyone by denying that they degraded performance for cost saving purposes — but if so, this paper doesn't provide evidence of it. Still, it's a fascinating study of the unintended consequences of model updates.
Earlier quoted context omitted.
I mean, they could be lying. Or only talking about the API version and not the front-facing ChatGPT.
I feel like it would be a fairly large conspiracy by the OpenAI team though? In fact, what motive would they have to make the model dumber, really? If it gets out and you're right, I think it will cause major trust issues with the product.
Earlier quoted context omitted.
The mixture of experts approach was found to be inferior than a straightforward transformer. However, researchers discovered that MoE models give substantially better results when guided by fine-tuning. My guess is that GPT-4 was an experiment to prove out that theory at huge scale and I also suspect that its performance astonished OpenAI as much as everyone else.
If it was an experiment I'd like to know why I'm still paying for access to something claiming to be it.
Title: "GPT-4 is getting worse over time, not better" Paper title: "How Is ChatGPT’s Behavior Changing over Time?" Paper Abstract: "GPT-3.5 and GPT-4 are the two most widely used large language model (LLM) services." When are people gonna realize that GPT-4/3.5 != ChatGPT As far as I can tell, the paper doesn't explain the methodology either, so hard to know if they're actually using "raw" GPT-4 or GPT-4 via ChatGPT.…
Every time a LLM is fine-tuned it gets stupider and less capable compared to the bare model. openai's legal and social ass covering attempts to neuter their model's output via fine tuning have done the same.
That is blatantly false, these models only work as well as they do in the first place because of instruction fine tuning. The raw models are much harder to work with.
It is unclear why this may be, but to speculate, it may be due to classifying large regions of the distribution as 'off limits' and therefore less structure about these regions is modeled in detail (since it is now not needed, and summed up as 'as a LLM, I cannot'). It is certainly strange that making a model less deplorable will make it worse at coding in general, but it does appear to be a real observation.
I have not read the paper yet (in my backlog, here's the paper: https://arxiv.org/pdf/2307.09009.pdf ), but it's important note that the paper is entitled "How Is ChatGPT’s Behavior Changing over Time?" not that it's necessarily "getting worse." Here's a more nuanced (not an AI clout chasing account) discussion by Arvind Narayanan (Princeton CS prof) about the results: https://twitter.com/random_walker/status/1681489…
How does Code Interpreter work vs base GPT-4 for code snippets? I'm writing questions and pasting context code into GPT-4 right now, and it works pretty well.
The linked twitter account is an AI influencer, so take whatever is written with a grain of salt. Their goal is to get clicks and views by saying controversial things. This topic has come up before, and my hypothesis is still that GPT-4 hasn't gotten worse, it's just that the magic has worn off as we've used this tech. Studies to evaluate it have gotten better and cleaned up mistakes in the past.
Earlier quoted context omitted.
So you are just going to ignore the data (not anecdotes) presented in the SP?
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Person A offers a reason why we may want to be slightly more skeptical than normal about this information. Person B suggests we can pretty easily look past that. Person C (you) injects themselves in the conversation simply to say that they can't add anything to the conversation. The closest example I can think of that is equally cringe-inducing is two people talking about a show and an unasked third party leaning in to add, "well, I don't have a television so I can't comment."
I modify it to request a draft... and it does it.
There are some guard rails being put in. I think the experience of using this from the first moment (or close to) it was available also feels different.
Running one's own prompts again if you have tried different things is worth while.
Also comparing the output from the API to the Web interface is something I haven't had a chance to look into.