[HN Gopher] Fine-tuning now available for GPT-4o
___________________________________________________________________
Fine-tuning now available for GPT-4o
Author : davidbarker
Score : 191 points
Date : 2024-08-20 16:50 UTC (6 hours ago)
(HTM) web link (openai.com)
(TXT) w3m dump (openai.com)
| hubraumhugo wrote:
| Curious to hear some real-world use cases for fine-tuning and
| what your setup looks like in terms of:
|
| - frameworks
|
| - hardware
|
| - foundation models (OS vs 3rd party)
|
| - evaluation & training data
|
| Did the performance gains justify the investment for fine-tuning?
| darksaints wrote:
| I think vision models have a lot bigger ROI from fine tuning
| than language models. That being said, I do consider fine-tunes
| to be helpful in improving smaller models as long as the domain
| is limited in scope. In other words, fine tuning allows you to
| get similar performance in smaller models, but improved
| performance in larger models seems pretty elusive at the
| moment, albeit somewhat possible with very creatively
| engineered training datasets.
|
| An example of this is DeepSeek-Coder, which can essentially be
| considered a fine-tune of a fine-tune of a Mixtral model. It
| performs very similarly to Claude 3.5 Sonnet, which is pretty
| damn impressive, but it does it at less than 1/10th the cost.
|
| What I don't understand though is why anyone would even
| remotely consider fine tuning a GPT-4o model that they will
| never fully own, when they could spend the same resources on
| fine tuning a Llama3.1 model that they will own. And even if
| you absolutely don't care about ownership (???), why not do a
| fine tune of an Anthropic model which is already significantly
| better than GPT-4o. At this point, with the laggard performance
| of OpenAI and their shameless attempts at regulatory hostility
| to competitors, I can't imagine ever giving them any of my
| money, let alone owning my derivative work.
| simonw wrote:
| "An example of this is DeepSeek-Coder, which can essentially
| be considered a fine-tune of a fine-tune of a Mixtral model"
|
| I've not heard that anywhere else. My impression from
| https://huggingface.co/deepseek-ai/deepseek-coder-33b-base
| was that DeepSeek-Coder was trained from scratch.
| darksaints wrote:
| The current deepseek-coder version (v2) is actually a fine
| tune off of the deepseek v2 model, and was not trained from
| scratch.
|
| I'm now getting conflicting information about the origin of
| the deepseek MOE framework, so I may be wrong about it
| starting with a Mixtral model.
| jstummbillig wrote:
| When Cosine fine tunes a model to be generally better at coding
| than gpt-4o that leaves me vaguely confused about the role that
| each part plays in building the best LLMs (and I would not be
| shocked to learn that this confusion was also SOTA)
| everforward wrote:
| I don't think it's all that surprising. VRAM is much smaller
| than all the data the bot was trained on, so some stuff gets
| dropped or "lossily compressed". In the case of foundation
| models, this is likely a bit of everything.
|
| Fine tunes take that and change what gets dropped or
| compressed. Rather than somewhat evenly forgetting things, it
| forgets very little of the knowledge they're targeting (like
| coding) and in exchange drops much more of "everything else".
|
| I don't believe it works this way, but in a sense, it's like
| GPT4o is a 400B parameter model but only devotes 20B parameters
| to coding because the rest are taken up by Wikipedia and
| knowing French and what not. A 70B coding fine-tune might be
| able to devote more than 20B parameters to coding, in exchange
| for only speaking English, having very little encyclopedic
| knowledge, etc.
|
| It's kind of like CPUs vs ASICs. CPUs (GPT4o) will perform
| better on average at random tasks, while ASICs (finetunes) do
| dramatically better on the task they were built for, even if
| they have a lower transistor count.
| jstummbillig wrote:
| But is that not what MoE is supposed to solve already? Why no
| it bake whatever Cosine does into the coding expert?
| johntb86 wrote:
| GPT-4o has seen a lot of examples of what people write about
| coding on the web, what code exists, and what tasks people want
| to do with that. But that general data doesn't include full
| process of coding - get a bug report, look at a codebase
| beforehand, make changes to the codebase, test them and see
| exactly what bugs they have, and iterate. That process is what
| SWE-bench tests.
|
| It's possible OpenAI did some coding fine-tuning themselves;
| Meta's Llama 3 paper [0], section 4.3.1 mentions what sort of
| work is needed. However, anything OpenAI did is based on their
| own tooling and set of assumptions - e.g. how is the existing
| code input into the LLM, what set of actions can the LLM take
| (e.g. look up documentation), what language is the output code
| being written in, etc. Cosine's LLM framework may do things
| differently and have different features, so you'd need to fine-
| tune the LLM to take maximum advantage of the framework.
|
| It's like dropping the LLM down in front of Vim when it had
| only ever used or even heard of notepad (or even emacs); there
| needs to be some training to make it work well with the new
| tools it has.
|
| [0]: https://arxiv.org/pdf/2407.21783
| simonw wrote:
| I'm still looking forward to a fine-tuning vendor publishing an
| interactive real-world example of one of their fine-tuned models,
| accompanied by the exact training data that was used to create
| it.
|
| OpenAI's examples here are artificial, and there's no mechanism
| to try out the results:
| https://platform.openai.com/docs/guides/fine-tuning/fine-tun...
|
| Fine-tuning is expensive in terms of both time and money (more so
| time these days, a lot of the vendors have free trials now).
| Before I put that work in I want to get a much better idea of the
| kind of results I can expect.
| bob1029 wrote:
| Fine tuning always felt like something that only works at
| incredible scale with a lot of data.
|
| Prompt engineering, RAG, etc. provide so much more uplift per
| unit time invested. Maybe fine tuning makes sense if you have a
| 9 figure budget and thousands of humans to throw at it. I don't
| feel this works in a typical startup ecosystem. I couldn't move
| the needle with the amount of data I had on hand.
| btown wrote:
| To this point, guaranteeing that your fine-tuning doesn't
| actually _weaken_ the hallucination-resistance of state-of-
| the-art base models, by overfitting to specific examples in
| your training set, is a really hard thing to measure. Unless
| you have robust ways of validating LLM output so that you can
| do real cross-validation, few-shot prompts, where you can
| instruct the LLM exactly how much you want it to generalize
| from those examples, may be far better at achieving many
| goals.
| qudat wrote:
| What about techniques like LoRA?
| bob1029 wrote:
| I thought these models were already leveraging this
| technique (or something approximating it) as part of their
| fine tuning offering.
| boredemployee wrote:
| perhaps my ignorance and my beginner level on the topic are
| biasing me, but in my experience, fine-tuning the GPT-3.5
| turbo has elevated our operation to another level. When we
| put it into production, it became much faster, more accurate,
| and cheaper when compared to original gpt4. Our use case is
| to make unstructured data return a JSON with some key-value
| entities where the order matters.
| satvikpendem wrote:
| How are people feeling with regards to GPT-4o versus Claude 3.5
| Sonnet? I recently watched this Primeagen video [0] about how,
| because LLMs don't _actually_ understand anything (yes, AI Effect
| included [1]), one does not actually gain as much usefulness as
| they 'd expect, especially with subtly wrong outputs. Over time,
| it just wastes way more time and becomes a form of learned
| helplessness (and yes, I do know about Socrates' dialogue, I saw
| it originally elsewhere on HN and had been quoting it for some
| time [2]).
|
| [0] https://www.youtube.com/watch?v=1-hk3JaGlSU
|
| [1] https://en.wikipedia.org/wiki/AI_effect
|
| [2] https://news.ycombinator.com/item?id=40920318
| carbocation wrote:
| I feel like this comment was made in good faith so I wanted to
| explain my downvote: I think it's just too far off-topic for
| this particular announcement. It's better as a separate
| discussion.
| satvikpendem wrote:
| Thanks, however I thought it was fairly aligned simply due to
| 4o receiving the same sorts of functionality as Claude had
| for a while.
| logicchains wrote:
| I'll also explain my downvote, it's because of the assertion
| that LLMs "don't actually understand anything", which to anyone
| who's actually successfully used LLMs to solve a difficult
| problem is clearly false, unless you use some contrived
| definition of the word "understand" that doesn't match how the
| word is actually used in normal conversation.
| teaearlgraycold wrote:
| The bigger ones have gained a rough understanding of a few
| systems [1]. Which is really impressive and gives an answer
| to the Chinese Room experiment. In my experience they don't
| understand a lot of things I ask about very well. But the
| fact that they understand anything at all is impressive.
|
| 1. https://danangell.com/blog/posts/gpt-understands/
| logicchains wrote:
| If five years ago someone said that in half a decade we'd
| have a computer program that could solve medium-complexity
| Leetcode problems that it had never seen before, hardly
| anyone would believe them. Now we have programs that can do
| exactly this, and yet some people never miss a chance to
| try to trivialise what just a few years ago would have been
| considered an amazing, world-changing achievement.
| torginus wrote:
| Can it though? My understanding is that ChatGPT has all
| the Leetcode problems memorized, maybe it can extrapolate
| to substantially similar ones in its training set.
|
| I tried it for advent of code 2023, and it was pretty
| helpless.
| KoolKat23 wrote:
| As do most people.
| torginus wrote:
| I don't think that's true - I think decent programmers
| can figure out mediums if they apply themselves.
| satvikpendem wrote:
| I am referring specifically to what was claimed in the video
| I quoted, so, unless you watched the actual video, what you
| are saying has not much bearing on what I am actually saying.
| Sorry to say it, and not to be harsh, but I constructed my
| comment to specifically point to such an instance via bracket
| quotes, please respond to what was said in said video.
| simonw wrote:
| Asking people to watch a 30m video in order to understand
| your comment isn't reasonable - can you summarize the point
| from the video that you're arguing here so people can
| respond to it without putting in all of that extra work?
| satvikpendem wrote:
| Well, I did; LLMs are not necessarily intelligent enough
| to not cause new problems in terms of the solutions they
| produce. This is a fundamental flaw of LLMs that is
| covered even by mainstream media, much less the AI Effect
| as shown by Wikipedia. At worst, they might turn a 0.1x
| engineer into a 10x one, ie a 1x one, except with no
| ability to actually solve problems cohesively.
| simonw wrote:
| I'm an experienced engineer and I've seen what I estimate
| to be a 2-5x productivity improvement in the time I spend
| typing code into my computer from embracing LLM-assisted
| development.
|
| Typing-in-code is only 10% of the work that I do, but
| this is still a very meaningful improvement for me.
|
| I've written a bunch more about my own experiences here:
| https://simonwillison.net/series/using-llms/ and here:
| https://simonwillison.net/tags/ai-assisted-programming/
| bearjaws wrote:
| https://myswamp.substack.com/p/ymxb-is-hard-for-llms
|
| I recently made this blog post showing how y=mx+b is very error
| prone in GPT4o, and pretty accurate (to a point) in Claude 3.5.
|
| I haven't gone down the rabbit hole yet, but I was wondering if
| fine tuning could fix math errors in LLMs. My initial hunch and
| understanding is it will not. I'll have to give your links a
| read/watch.
| kkukshtel wrote:
| Through this same lens, using Google is a form of learned
| helplessness.
| satvikpendem wrote:
| If you follow [3], yes, even learning to read and write is a
| form of learned helplessness, as Thoth and Socrates conclude.
| Now, you might not thing that affects our day to day world,
| but it does; because imagine if we could not read nor write,
| what sorts of hypotheses we might come up with, now extend
| that to what we say about LLMs. To those that say those are
| inequal circumstances, I invite you prove why. Thoth and
| Socrates know a hell of a lot more than you ever did, to be
| frank.
| tines wrote:
| What kind of data or data formatting do you need to fine-tune
| GPT-4o? I'd love to throw a bunch of documentation at it and let
| it learn, but I don't have the resources to extract knowledge,
| format it as questions and answers, etc.
| simonw wrote:
| Fine-tuning generally isn't an effective way to add extra
| knowledge from things documentation - my understanding is that
| the vast amounts of knowledge in the original training data
| tend to overwhelm any extra knowledge you try to add by fine-
| tuning.
|
| OpenAI's documentation has good examples of how the data should
| be formatted (and when it's appropriate to fine-tune):
| https://platform.openai.com/docs/guides/fine-tuning/fine-tun...
| qudat wrote:
| does the same result apply to LoRA?
| qeternity wrote:
| Fine tuning is most often performed by training a LoRA.
| It's almost certainly what OAI are doing, as they can
| inference many lightweight LoRA in parallel atop the same
| foundation model.
| floam wrote:
| https://platform.openai.com/docs/guides/fine-tuning
| gamegoblin wrote:
| Seems like their competition (Google, Anthropic) have both
| shipped prompt caching, which is a lot more developer-friendly
| and gets a lot of the same benefits as fine-tuning.
|
| You just cache a prompt with a ton of examples that you would
| have otherwise fine-tuned on. You can trivially update that
| prompt whenever, no asynchronous fine-tuning job needed.
|
| Wonder if OpenAI will stick with fine-tuning or go towards prompt
| caching (or both). Fine-tuning has uses, but prompt caching gets
| you 99% of the benefits for 1% the effort.
| eightysixfour wrote:
| They, in my experience, result in different outcomes. Fine-
| tuning is better at shaping the response, for example getting a
| correct JSON format, or making the responses shorters and
| concise, or use emojis, or whatever. Long prompts are better
| for in-context learning, fine-tuning doesn't seem to impart
| knowledge well, it just increases or decreases the likelihoods
| of existing knowledge coming out.
| maeil wrote:
| Genuinely curious as you have experience with fine-tuning,
| which I don't yet. Given how simple it has become to get the
| correct response shape, do you feel there's still a point to
| it? If you need JSON and want to be very sure you can just
| use function calling, if you only need simple boundaries, use
| Claude-like XML, if it's something very complicated you can
| give a few shots.
|
| From my understanding, fine-tuning allows for reducing model
| size, meaning less latency and cost. That seems like it would
| be the biggest advantage, no?
| eightysixfour wrote:
| > Given how simple it has become to get the correct
| response shape, do you feel there's still a point to it?
|
| I am personally working on systems which don't do mrequire
| uch structured data output; my work is more around
| structure, style, etc. Fine-tuning is more effective and
| more consistent for me in those contexts.
|
| One thing to remember is that the big models, like 4o and
| Claude, both are fine-tuned for chat interactions. This
| tuning can often make them dumber, more verbose, etc.
| because that's what the human feedback testing likes. You
| can look at some of the rating tests on chatbot arena as an
| example where two give the same answer, but people have
| voted more favorably for the one that delivers it with
| "more personality."
|
| There are SLMs that are better tuned for specific use cases
| and outperform the big models as a result of this, and the
| examples OpenAI showed in the article make it clear the big
| models can benefit from this as well.
|
| > From my understanding, fine-tuning allows for reducing
| model size, meaning less latency and cost. That seems like
| it would be the biggest advantage, no?
|
| This is partially correct, fine-tuning can be a step of
| this process. The full pipeline looks more like:
|
| 1. Create a prompt which gets a large model to output
| (mostly) correct responses.
|
| 2. Build a dataset of those inputs/outputs, probably with
| human or LLM curation/judging in the loop since some will
| still be wrong.
|
| 3. Fine tune a small model on those inputs/outputs.
|
| Now you have a smaller model which behaves more like the
| large model you were able to prompt engineer into
| instruction following.
| gamegoblin wrote:
| I agree that fine-tuning isn't good at imparting knowledge,
| though in my experience, K-shot prompting is very close to as
| good as fine-tuning at getting output formats (for recent
| models, at least)
|
| So except for cases where you are _really_ trying to
| perfectly match a particular writing style and want to fine-
| tune on loads of some author 's text, I think prompt caching
| mostly dominates fine-tuning for most real-world workloads.
|
| I'd still fine-tune if I wanted my model to have a really
| particular "voice" though.
| WanderPanda wrote:
| I would have expected fine-tuning to be good at imparting
| knowledge. Pre-training is often done for a single epoch only
| and models soak up the knowledge like crazy without multiple
| passes so why would fine-tuning be any different?
| eightysixfour wrote:
| Because the learning rate vs. pre-training is completely
| different. This is not accurate, but my mental model is
| that the LLM's initial training establishes the "space of
| concepts and ideas" while tuning (like RLHF and fine-
| tuning) changes how it expresses those concepts and ideas.
| It works well for me in deciding my approach.
| leobg wrote:
| The problem is that it's almost impossible to teach
| knowledge to an LLM without teaching a specific form of
| expression at the same time.
|
| When you have a question/answer tuple in your training
| data, you are also teaching the model that every other way
| of answering the question is wrong.
|
| So while the LLM would probably be capable of generating
| maybe 100 answers to the question that would be equally
| useful (just using different phrasing, different choice of
| words, etc.) come on, you are forcing it to update its
| parameters to suppress all of these, except the one
| specific form that you selected.
|
| So you're not really adding knowledge. Instead, you're
| chiseling knowledge away.
| eightysixfour wrote:
| I'm not sure this is entirely true, but I guess we could
| test it by generating a large enough dataset around a
| specific concept, and see if we can add that concept to a
| model. Or change one that exists to something else
| entirely.
|
| For example, create an "idea", generate thousands of Q&A
| pairs about that idea from different angles, and generate
| conversations about that idea, then train the model on
| it. This is essentially the Phi process, but with a
| single concept instead of "everything."
|
| My guess is that fine-tuning cannot add the concept to
| the model without suffering from catastrophic loss
| everywhere else. However if we added that same data to
| the pre-training dataset and retrain the model, the model
| would express the idea correctly.
| esafak wrote:
| Does their prompt caching work with semantically equivalent
| queries, or do they match lexically?
| simonw wrote:
| You have to mark exactly which parts of the prompt you want
| to cache.
|
| The only vendor I've seen with "automatic" prompt caching so
| far is DeepSeek https://platform.deepseek.com/api-
| docs/news/news0802/
| KTibow wrote:
| OpenAI has a mysterious reference to caching in their guide to
| latency.
|
| > Maximize shared prompt prefix, by putting dynamic portions
| (e.g. RAG results, history, etc) later in the prompt. This
| makes your request more KV cache-friendly (which most LLM
| providers use) and means fewer input tokens are processed on
| each request.
|
| source: https://platform.openai.com/docs/guides/latency-
| optimization
| okl wrote:
| Sounds very similar to how e.g. docker/layers work - the
| later you put the more dynamic stuff, the higher the change
| that previous layers are reused. Meaning the cache entries
| are likely chained, i.e., each one depends on the preceding
| entry.
| htrp wrote:
| How do you know it's not prompt caching in the backend?
| simonw wrote:
| OpenAI haven't documented if they do this, but it should be
| reasonably easy to determine via experiments against their
| API.
|
| Claude's prompt caching gives a very material performance
| improvement - they claim up to a 4x performance boost in
| https://www.anthropic.com/news/prompt-caching - so if OpenAI
| have similar techniques, even undocumented, they should
| become visible through sending the same prompt a bunch of
| times and measuring the latency.
| NavinF wrote:
| OpenAI docs say they also improve latency with caching:
| https://news.ycombinator.com/item?id=41302358
| nprateem wrote:
| IMO they are definitely doing something. I've had countless
| occasions where the first few times I run a prompt I get
| excellent output, then several tries later it just returns
| the same dumb-sounding AI shite.
| impossiblefork wrote:
| It's also pretty much required if you want to do anything
| complicated with prompt networks or agent-type stuff.
|
| Without caching it's too expensive.
| thomasahle wrote:
| Do any vendors allow fine-tuning on images yet?
| Someone1234 wrote:
| When are they going to fix ChatGPT Classic not using Custom
| Instructions?
| chartpath wrote:
| Some much focus on fine-tuning when it can actively make
| performance on reasoning and planning benchmarks worse (over a
| baseline of already worse-than-coin-toss).
|
| Why not give us nice things for integrating with knowledge graphs
| and rules engines pretty please?
| simultsop wrote:
| Just catching up the phrase of marketing "this is just the start
| ...". Isn't this overused?
| underyx wrote:
| How do y'all deploy fine-tuned models? I have separate projects
| for staging and prod, but it doesn't seem like a fine-tune can be
| shared across projects.
|
| Am I wrong to split projects by env? Am I expected to run fine-
| tunes separately per env (surely not)? Am I missing an option to
| share fine-tunes across projects?
| simonw wrote:
| Are you talking about fine-tuned models that you host yourself,
| or fine-tuned models from a hosted provider like OpenAI?
|
| What do you mean by an "env" here?
| underyx wrote:
| I mean OpenAI hosted fine-tunes (same as referenced in OP.)
|
| I have a staging deployment and a production deployment.
| Ideally anything that I roll out to production, I can try on
| staging first -- including switching from gpt-4o to a fine-
| tuned gpt-4o. I don't want the production API key to be
| accessible by my staging app, so I have two separate projects
| within the OpenAI dashboard. One is called my-app-prod, and
| the other is my-app-staging.
|
| To illustrate the problem further, I also have infrastructure
| to eval models before changing what production is running.
| The eval infrastructure also has its own OpenAI project, so
| that I can set separate billing limits on running evals. Any
| fine-tuned model needs to be benchmarked first, but again,
| I'm not sure how to make the same fine-tune available to both
| the eval project, and the production app project.
| FlamingMoe wrote:
| It's wild how quickly the AI landscape shifts. Just 6 months ago,
| I couldn't go a day without using ChatGPT. Now, it's like finding
| an old flip phone in a drawer. I've gone full Claude convert.
| Wonder where I'll be six months from now.
| aantix wrote:
| Github's Copilot Workspace or Cursor's new composer beta
| feature. That's where you'll be in 6 months.
|
| Multi-file code suggestions with an intimate understanding of
| the entire code base.
|
| https://x.com/aantix/status/1819794837375263228
|
| P.S. Or Plandex, https://plandex.ai/
| rd wrote:
| Cursor + Sonnet feels like a cheat code right now. Feels
| similar to the excitement I had the first day I ever used
| GitHub copilot.
|
| I saw this this morning - it really made me think about what
| the future of 1-man non technical founder startups will look
| like. https://x.com/0xluffyb/status/1825854097481736479?s=42&
| t=7-X...
| greenchair wrote:
| I was going to try copilot workspace till during signup
| process it asked for access to my private repos then noped
| out of there. Why would they need that unless they wanted to
| use it to train off my data?
| bachback wrote:
| how you find it better? haven't seen the benefits
| machiaweliczny wrote:
| I have sub to Claude now due to projects/artifacts (caching
| really) but aider with new gpt4o works well
| ziofill wrote:
| I think I'm not understanding what goes into making this
| available. Fine-tuning is a known technique, why is it available
| "only" now?
| HarHarVeryFunny wrote:
| Because this isn't you fine-tuning an open weights model and
| running it yourself - this is fine-tuning via OpenAI's API and
| them hosting the resulting model (and everybody else's
| differently fine-tuned ones) and running it for you.
| WhitneyLand wrote:
| Say I want to ask a model about a code base 500k tokens in size.
| The questions would not be to generate code, but rather to help
| understand it in a way that require the model to globally reason
| about everything at once.
|
| How might performance compare between:
|
| 1.) Using a model like Gemini to load it all into context at once
|
| 2.) Using one of the various summarization systems/embedding/RAG
| etc.
|
| 3.) Fine tuning the whole code base into Gpt-4o
| spullara wrote:
| 1 then 2. Never 3.
| WhitneyLand wrote:
| Interesting why would you say that?
|
| There's not been much opportunity previously to easily fine
| tune a Gpt 4 class model. I haven't seen anything written up
| on this being tried.
| Liquix wrote:
| tools like aider [0] maintain a large context with minimal
| token usage by reading a repo's .git folder. impressive results
|
| [0] https://github.com/paul-gauthier/aider
| trees101 wrote:
| also, aider has added experimental support for prompt
| caching: https://github.com/paul-
| gauthier/aider/issues/1086#issuecomm...
|
| I really like the control you get with aider over the LLM
| context. You can /add or /drop source code, markdown notes.
| You can /clear the chat. /tokens shows you the context and
| the cost, you can see what each prompt will cost you.
|
| I find aider best used in conjunction with a git diff view in
| VSCode, I run aider with --no-auto-commits and then manually
| review each time in VSCode.
|
| I'm keen to learn any AI coding workflows if anyone has any
| links. I've benefitted greatly from tips such as using type
| hints and documentation for the LLM's benefit.
| machiaweliczny wrote:
| I have 5 lines shell to load git into single file and using
| google AI studio. Tested it to simplify dreamcoder
| implementation but results are ~ so far
| highfrequency wrote:
| Huh? It says: "Cosine's Genie achieves a SOTA score of 43.8% on
| the new SWE-bench Verified benchmark" with a link to
| https://www.swebench.com/
|
| But the SWE-bench leaderboard (linked to in the post) doesn't
| show Cosine Genie at all, instead showing Amazon at the top with
| 38.8% accuracy.
|
| If true, seems wild that Genie, a 10 person startup with $2.5m in
| funding can actually achieve SOTA results over Google, Amazon,
| Microsoft, Anthropic, OpenAI etc. It's not like the big players
| are overlooking this problem of using LLMs to automate software
| engineering. Anyone have more color on this? I see some
| speculation online that the training data can easily get
| contaminated with benchmark questions, but not much careful
| evidence.
| jstummbillig wrote:
| "verified" being the (rather confusing) keyword here.
|
| https://openai.com/index/introducing-swe-bench-verified/
| highfrequency wrote:
| Your link doesn't actually point to the leaderboard. This
| link does https://www.swebench.com/ and you can click on the
| "Verified" tab. I don't see any entry for Genie.
| hansonw wrote:
| It looks like they didn't want to make a public submission in
| order to avoid disclosing the model internals:
| https://cosine.sh/blog/genie-technical-
| report#:~:text=SWE%2D....
| JamesBarney wrote:
| It's with an asterisk. Here's their comment from cosine's
| website.
|
| Note SWE-Bench has recently modified their submission
| requirements, now asking for the full working process of our AI
| model in addition to the final results -their condition to have
| us appear on the offical leaderboard. This change poses a
| significant challenge for us, as our proprietary methodology is
| evident in these internal processes. Publicly sharing this
| information would essentially open-source our approach,
| undermining the competitive advantage we've worked hard to
| develop. For now, we've decided to keep our model's internal
| workings confidential. However we've made the model's final
| outputs publicly available on GitHub for independent
| verification. These outputs clearly demonstrate our model's 30%
| success rate on the SWE-Bench tasks.
| Der_Einzige wrote:
| There seems to be no information on _how_ this fine-tuning is
| implemented, and this matters, because parameter efficient
| techniques minimize catestrophic forgetting, which is most of
| what causes fine-tuned models to perform worse.
|
| Until this happens, and until we have flexibility of fine-tuning
| techniques, I will push investors still towards PyReft, Lora,
| related, and the open source models where all of these techniques
| work.
| Nuzzerino wrote:
| > Fine-tuned models remain entirely under your control
|
| Also on the very next paragraph:
|
| > We've also implemented layered safety mitigations for fine-
| tuned models to ensure they aren't being misused.
|
| Well done, Sam
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