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The False Promise of Imitating Proprietary LLMs
- mxwsn 3y agoThis is an important study and I've been waiting for something like this ever since Alpaca and the following wave of imitating models that have had lackluster, non rigorous evaluation.
- ImprobableTruth 3y agoThis isn't a new result really. We already know through the gpt-4 paper that rlhf style fine-tuning just makes the model more compliant, not more capable.
- YetAnotherNick 3y agoExactly. Few hundreds of thousands of interactions with chatgpt is definitely too less for model to learn lot of new things. The thing it does well is make them much better at following instructions. It also makes it much better at working with given context.
- luckystarr 3y agoConspiracy theory: Is that the reason why GPT-4 is not available as an API? So people wouldn't siphon off it's capabilities?
- Semaphor 3y agoBut it is, waitlisted-gated, but available. I have API access.
- nmfisher 3y agoI think it is available now.
- arugulum 3y ago1. It is available via API 2. Likely not a conspiracy theory. Newer models don't have logits available and that's almost certainly because they didn't want other labs distilling from them.
- deleted 3y ago[deleted]
- luckystarr 3y ago> Newer models don't have logits available [...] This definitely is a smoking gun. Keeping the crown jewels for themselves.
- int_19h 3y agoNot only it is available, but people have been fine-tuning LLaMA using GPT-4 outputs and training data generated by it for a while now; that's why you see models with names like gpt4-x-alpaca on HuggingFace.
- cs702 3y agoThe authors conduct automated, more methodical evaluations of LLMs finetuned to imitate ChatGPT outputs, and find that, despite superficial/informal appearances to the contrary, the base LLMs close little to none of the gap to ChatGPT on tasks that are not heavily supported in the imitation data. It's not good news for the open LLM ecosystem.
- mdale 3y agoI don't know if it's bad news per say. It helps to know where to deploy a tool, it's limitations and where to focus to build something competitive / better.
- deleted 3y ago[deleted]
- flangola7 3y agoGood news for alignment though. This gives me a tiny amount of hope.
- int_19h 3y agoSo, LLMs aligned with the interests of our corporate overlords and that nebulous "national security" thing that somehow always translates to more surveillance and less due process?
- flangola7 3y agoThis tech has about as much chance to continue unregulated as highly enriched uranium. There is no future-path that includes unregulated AI. I don't like horrific government abuse of residents,and I would not mind throwing most billionaire CEOs into a pool of alligators and dissolving their corporations. I don't like Altman, I think he's a smart person with NOBUS-level reckless hubris who is softballing the magnitude of the dangera to wet. The status quo is not good and it's getting worse.z It doesn't matter. 5 people with launch-all-the-nukes buttons is better than 500 million.
- throwaway6977 3y agoIf they really didn't test anything bigger than 13b, as their abstract states, then this doesn't even seem worth reading through.
- wmf 3y agoThe "Google has no moat" thing claimed that Vicuna-13B was almost as good as ChatGPT and this paper seemingly refutes that.
- ShamelessC 3y agoClaims made in a leaked blog post shouldn't be considered as having any sort of scientific authority. That whole "no moat" piece has exactly the tone I would expect from an over-confident Googler who has essentially been following all of this by watching various Discord channels and browsing hacker news. That isn't how science is done. It shouldn't be how business is done, but people seem to really enjoy these everything-is-actually-simple narratives.
- space_fountain 3y agoThat claim wasn't sourced from the Google has no moat paper, but from the announcement for Vicuna-13B if I recall (or some other similar model). It shouldn't be taken as an independent quality assessment
- nptacek 3y agomy eyebrows went up at a number of choices made in their assessment
- Sai_ 3y agoCould you explain what other choices were red flags for you? I’m somewhat familiar with the open source LLMs space but not enough to know why some choices are better than others.
- nmca 3y agonobody with lots of experience with proprietary LMs is surprised
- Sai_ 3y agoAnd neither is anyone who has played with these new LLMs, found them so-so, and wondered whether the hype was warranted.
- devjab 3y agoI’m curios as to why you think the hype isn’t warranted. If you go through my history (you don’t have too I’ll sum it up), you’ll see that I’m not impressed by the capabilities of LLM to actually do my work. Not for a lack of trying, but because ChatGPT simply tells too many lies. We’ve yet to get it to really do anything that wasn’t fairly basic, or solved a billion times on the internet anyway. Similarly we’ve stopped using co-pilot because it takes too much time to make it go away when it’s being bad to make up for the good it does. Or to put it differently in SWE the LLM seem very bad at building things. What they are good at, however, is helping us build things. I’m not sure I’ll ever need to write JSDoc again on anything that isn’t too sensitive to share. Which is a significant efficiency and quality improvement on the work I do. I think of them as Swagger generators, but instead of being for an OpenAPI standard they are for everything. I imagine they’ll become very good at automating testing as another example, which will again be a further improvement on the work a single developer does. In terms management might understand. I think you can view LLMs similarity to the way we’ve seen frameworks and tooling reduce the team size needed to build an application significantly over the previous 30 years. If you wanted to build a web-portal for asset management in 1999 you’d need a large team to do what a single developer and a good PO can do today. Maybe we won’t see the same reduction manpower, but instead an increase in quality.
- Sai_ 3y agoI meant the hype around open source LLMs, not OpenAI's LLM. On reading your response and my original comment, I suspect you thought I was including OpenAI's LLM as a hype-driven product. Sorry if you didn't think that. That said, the rest of your comment is spot-on. Paul G says this too that ChatGPT expertise is the same as a journalist's expertise. Its output seems impressive until it is on a subject you know very well. GPT-x is like a wide-eyed intern or junior team member who loves to shoot its mouth because it has been told to be assertive and vocal. The good thing is that it is willing to learn. Now, if this is true of GPT-x which is pretty much the benchmark against which every open source LLM is being measured, you can guess for yourself how much room these open source LLMs still have to cover.
- blazespin 3y agoThe breathtaking audacity of calling distilling GPT4 'stealing' when GPT4 trained on data it has no proprietary right to.
- runsWphotons 3y agoyeah this is insane thinking haha
- kordlessagain 3y agoJust because someone can convert text to numbers doesn’t mean they have a right to the numbers. That’s like trying to own the emotion a book has on someone, or the things they see in mind when they read it.
- blazespin 3y agoWhat I find rather amusing is they spend the whole paper dismissing it as ineffective yet still feel the need to worry about the 'ethics' and 'legality'. They don't cite anything with regards to a discussion/evidence of either, of course, and looking at the authorship list I don't believe any of them are lawyers or ethics experts.
- jehdbdbf 3y ago[flagged]
- quickthrower2 3y agoLike a torrent of the last GoT season then? … with compression.
- croes 3y agoImagine the GoT producers used GRRM's books without licensing and then claim copyright on the series. Does OpenAI have the rights on all the texts they used to train their GPTs?
- 3y ago
- ofou 3y agoFrom the Conclusion: "Finally, our work raises ethical and legal questions, including whether the open-source community should continue to advance progress by “stealing” what OpenAI and other companies have done, as well as what legal countermeasures companies can take to protect and license intellectual property." Really???
- croes 3y agoI think the creators of all the scraped training data would like to talk about intellectual property too
- senttoschool 3y agoI've written hundreds of thousands of words, possibly millions on various sites including HN, Reddit, blogs, Stackoverflow, forgotten platforms, etc. Doesn't seem right that OpenAI/LLMs can use my intellect but I can't use theirs.
- winddude 3y agotheir work didn't they did. I'm going to need verifiable proof this wasn't written by chatGPT as propaganda.
- RobotToaster 3y agoWas this "study" sponsored by "open"ai?
- deleted 3y ago[deleted]
- washadjeffmad 3y agoThat counts as a question, I guess.
- brucethemoose2 3y agoThe jump between llama 13B and 30B is quite significant. And their instruction finetuning is not SOTA I don't think, though the point about general knowledge is a good one: instruction llama lies very confidently. But one great thing about open source LLMs is that you can specialize them in various tasks with affordable LORA training, enough to easily beat GPT4 in a specific niche.
- ThorsBane 3y agoAny recommended starting points for LORA training llama 30B on a specific niche? Books, tutorials, videos are all appreciated. Thanks for your time!
- airgapstopgap 3y agoThis is exactly the reason OpenAI isn't afraid of the open-source community, like many kneejerk opponents of regulatory capture assume (they are probably still afraid of Google). Also why they still do the expensive and cumbersome RLHF training, instead of those deceptively cheap and fast finetunes. They understand their own tech and why there isn't free lunch. Recently, John Schulman explained the issue with behavior cloning and it's a very typical ML problem.[1] Basically: what are we training the model to do? The model updates after finetuning in a holistic manner, based on the sum total of its content and capability. Suppose GPT-4 can correctly answer to many requests because it knows correct answers, in the sense that it has something isomorphic to an internal knowledge graph and tools for querying it, and that graph contains sufficient data for its tools to derive an answer at inference. RLHF reinforces this behavior by constraining the distribution of outputs (essentially, steering the model away from applying inappropriate tools for respective inputs, e.g. employing fantasy-narrative or bad-yahoo-answers cognitive routines when asked something that looks like a straightforward factual question). Now suppose you teach LLaMA-13B to imitate those responses by SFTing it on a dump of successful GPT-4 conversations. But LLaMA doesn't have internals that would have enabled it to find the same answers; so on the object level it shallowly memorizes specific items of the post-training dataset, and on the meta-level it learns the stylistic flourish of a high-powered model. But it starts to hallucinate confident nonsense whenever you step out of the training distribution, because it doesn't actually learn to query its own knowledge graph. A little anthropomorphism won't hurt: you create an incapable impostor this way, a wannabe nerd, a character who is used to guessing the teacher's password and being praised, instead of understanding the subject, and keeps raising its hand whenever a question is asked, but is painfully clueless. Indeed, the early and cheap success of behavior cloning was a massive red flag unto itself. There's no way all the compute and data that went into training GPT-3/3.5/4 tier models can be substituted with gently demonstrating the attitude vector. If we had models that were markedly less capable but comparably honest, we would have reasons for hope that this line terminates in a genuine open-source peer competitor; instead, we have total fraud. It is a nontrivial task to have a model generalize epistemic honesty and not a lower-order behavior like clamping up and kowtowing or bullshitting from external examples; train it to say "I don't know" whenever it actually does not, but only then. There are clever approaches here, but they're not such a low-hanging fruit as what passes for open-source right now. 1. https://youtu.be/hhiLw5Q_UFg?t=685 https://youtu.be/hhiLw5Q_UFg?t=685
- winddude 3y ago"However, imitation falls short in improving LMs across more challenging axes such as factuality, coding, and problem solving." Brilliant observation captain obvious.
- winddude 3y ago"Second, given the large gap between LLaMA and ChatGPT (the latter model is faster, cheaper, and more accurate), " No it's not, llama would be cheaper and likely faster if you ran it on the same scale, actually there've been a few calcs done, that running llama 65b if you're at 100% usage is cheaper than 3.5turbo per token. Also comparing them for accuracy isn't fair comparison, one is a foundational model, one is an instruct tuned model. Perhaps compare llama 65b with gpt3.
- fomine3 3y agoIsn't it the comparison is like "My home PC server is far cheaper than EC2"?
- dspoka 3y agoSensational title that misrepresents the message in paper. However, when conducting more targeted automatic evaluations, we found that the imitation models close little to none of the large gap between LLaMA and ChatGPT. In particular, we demonstrate that imitation models improve on evaluation tasks that are heavily supported in the imitation training data. On the other hand, the models do not improve (or even decline in accuracy) on evaluation datasets for which there is little support. For example, training on 100k ChatGPT outputs from broad-coverage user inputs provides no benefits to Natural Questions accuracy (e.g., Figure 1, center), but training exclusively on ChatGPT responses for Natural-Questions-like queries drastically improves task accuracy. Just because this might not be the way to replicate the performance of ChatGPT across all tasks, it seems to work quite well on whichever tasks are in the imitation learning. That is still a big win. Later on this also works for factual correctness. (leaving aside the argument whether this is the right approach for factuality) For example, training on 100k ChatGPT outputs from broad-coverage user inputs provides no benefits to Natural Questions accuracy (e.g., Figure 1, center), but training exclusively on ChatGPT responses for Natural-Questions-like queries drastically improves task accuracy.
- blazespin 3y agoTo be fair, this paper has been made obsolete in its entirety with recent research. It's not really their fault, but folks need to start publishing faster as posters or something if they want to provide something relevant. A better title, knowing what we now, might be "To outperform GPT4, do more than imitating"
- lebek 3y agoLink to said research?
- courseofaction 3y agoDoes this mean that, if one wants GPT-4 quality outputs on a topic, one should specifically generate a dataset on that topic to fine-tune their own model? There's still room for closing the gap, but ultimately it's only going to be a pale imitation when the underlying model's representations aren't as useful.
- a0zU 3y ago>Grammatical error in the abstract.
- nologic01 3y ago> imitation models are adept at mimicking ChatGPT's style but not its factuality this is largely a pot calling the kettle black. The LLM game is not about not mimicking somebody else. It is about not being caught doing so :-)
- kamranjon 3y agoI'd be really curious what the authors of the recent (3 days ago) paper on QLora would think of this article? https://arxiv.org/abs/2305.14314 https://arxiv.org/abs/2305.14314 - they claim "Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99.3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU" Particularly this statement seems relevant: "We provide a detailed analysis of chatbot performance based on both human and GPT-4 evaluations showing that GPT-4 evaluations are a cheap and reasonable alternative to human evaluation. Furthermore, we find that current chatbot benchmarks are not trustworthy to accurately evaluate the performance levels of chatbots. A lemon-picked analysis demonstrates where Guanaco fails compared to ChatGPT."