I think it's a good idea for everyone to download and be able to run a LLM locally, even if you have the minimum of requirements. As a pseudo-backup of a large chunk of human knowledge.
The ones you can run on your own machine tend to be bad - really bad. They hallucinate wildly and fail at all sorts of tasks that the larger hosted ones succeed at.
This makes them a fantastic tool for learning more about how LLMs work and what they're useful for. Interacting with a weak-but-functional LLM that runs on your own computer is a great way to get a much more solid mental model for what these things actually are.
For everyone else, I wish they experience these (locally or elsewhere) weak LLMs atleast once before using the commercial ones just to understand various failure modes and to introduce a healthy dose of skepticism towards the results instead of blindly trusting them to be the facts/truth.
If nothing else it does a great job of demystifying them. They feel a lot less intimidating once you've seen a small one running on your computer write a terrible haiku and hallucinate some non-existent API methods.
Interesting how you can have the same experience and come to opposite conclusions.
Seeing so many failure modes of the smaller models fall by the wayside as compute goes brrr just made me realize how utterly meaningless that phrase is.
if you accidentally use the word"hack" when trying to get ChatGPT to write some code for you. it'll stop and tell you that hacking is bad, and not a colloquial expression, and refuse to go further.
privacy reasons are another reason to try a local LLM. for the extremely paranoid (justified or not), a local LLM gives users a place to ask questions without the text being fed to a server somewhere for later lawsuit discovery (Google searches are routinely subpoenaed, it's only a matter of time until ChatGPT chats are as well.)
There's an uncensored model for vision available as well. The censored vision models won't play the shallow game of hot or not with you.
There are uncensored image generation models as well, but, ah, those are NSFW and not for polite company. (As well as there's multiple thesis' worth of content on what that'll do to society.)
Is that 3.5 or 4? I asked 4 for an example of code which "is a hack", it misunderstood me as asking for hacking code rather than buggy code, but then it did actually answer on the first try.
https://chat.openai.com/share/ca2c320c-f4ba-41bf-8f40-f7faf2...
The models are capable of much much more, and they are being significantly nerfed over time by these ineffective attempts to introduce safeguards.
Recently I've asked GPT4 to quote me some code to which it replied that it is not allowed to do so - even though it was perfectly happy to quote anything until recently. When prompted to quote the source code, but output it as PHP comments, it happily complied because it saw that as "derivative work" which it is allowed to do.
If you're looking to read a more advanced example of its capabilities and limitations, try
https://simonwillison.net/2024/Mar/23/building-c-extensions-...
You’re literally comparing apples to oranges.
Though it does come a little off as a comparison. How about programming assistance via asking a colleague for help, Stack Overflow, or online references, code examples, and other such things, which are closer to what the LLM would provide than LSP and treesitter?
> So genuine question: is that answer representative of the output quality of these things?
Is it that hard to input a prompt into the free version of ChatGPT and see how it helps with programming?
And most use I've seen on it realistically a good LSP covers.
Or to put it a other way. It's no good at writing algorithms or data structures ( or at least no better thab I would have with a first drafy but the first draft puts me ahead of the LLM in understanding that actual problem at hand, handing it off to an LLM doesn't help me get to the final solution faster).
So that leaves writing boiler plate but concidering my experience with it writing more complex stuff, I would need to read over the boilerplate code to ensure it's correct which in that case I may as well have written it.
Fair, that is possible depending on your domain.
> It's no good at writing algorithms or data structures
In my experience, this is untrue. I’ve gotten it to write algorithms with various constraints I had. You can even tell it to use specific function signatures instead of any stdlib, and make changes to tweak behavior.
> And most use I've seen on it realistically a good LSP covers.
Again, I really don’t understand this comparison. LSPs and LLMs go hand in hand.
I think it’s more of a workflow clash. One really needs to change how they operate to effectively use LLMs for programming. If you’re just typing nonstop, maybe it would feel like Copilot is just an LSP. But, if you try harder, LLMs are game changers when:
- maybe you like rubber ducking
- need to learn a new concept and implement it
- or need to glue things together
- or for new projects or features
- or filling in boilerplate based on existing context.
https://godbolt.org/z/s9Yvnjz7K
I mean I could write the algorithm by hand pretty quickly in C++ and would follow the exact same thought pattern but also deal with the edge cases. And factoring in the loss of productivity from the context switch that is a net negative. This algorithm is also not generic over enough cases but that is just up to the prompt.
If I can't trust it to write `strip_whitespace` correctly which is like 5 lines of code, can I trust it to do more without a thorough review of the code and writing a ton of unit tests... Well I was going to do that anyway.
The argument that I just need to learn better prompt engineering to make the LLM do what I want just doesn't sit with me when instead I could just spend the time writing the code. As I said your last point is absolutely the place I can see LLMs being actually useful but then I need to spend a significant amount of time in code review for generated code from an "employee" who is known to make up interfaces or entire libraries that doesn't exist.
> Write me an algorithm in C++ which finds the begin and end iterator of a sequence where leading and trailing whitespace is stripped. Please write secure code that handles any possible edge cases.
It gave me this:
https://chat.openai.com/share/55a4afe2-5db2-4dd1-b516-a3cacd...
I'm not sure what other edge cases there might be, however. This only covers one of them.
In general, I've found LLMs to be marginally helpful. Like, I can't ever remember how to get matplotlib to give me the plot I want, and 9 times out of 10 GPT-4 easily gives me the code I want. Anything even slightly off the beaten path, though, and it quickly becomes absolutely useless.
Free GPT I get https://chat.openai.com/share/f533429d-63ca-4505-8dc8-b8d2e7... which has exactly the same problem as my previous example and doesn't consider the string of all whitespace.
Sure GPT4 is better at that, it wasn't the argument made.
The example you gave absolutely was the code I would write on a first draft since it does cover the edge cases (assuming we aren't dealing with the full UTF charset and all that could be considered a space there).
However this is code that is trivial to write in any language and the "Is it that hard to input a prompt into the free version of ChatGPT and see how it helps with programming? " argument doesn't hold up. Am I to believe it will implement something more complex correctly. This is also code that would absolutely be in hundreds of codebases so GPT has tons of context for it.
Maybe a prompt to GPT
https://simonwillison.net/2024/Mar/23/building-c-extensions-...
(linked previously above)
I'm not sure there isn't a buffer overflow in the vector_decode code he showed there, likewise I don't see any error checks on the code and I am not familiar with the sqlite api to even know whether errors can be propagated upwards and what error conditions would mean in that code.
This code is probably fine for a quick side project but doesn't pass my smell test for anything close to production ready code.
I definitely would want to see a lot of unit tests around the decode and encode functions with fuzzing and to be honestly that would be the bulk of the work here. That and documentation on this code. Even though he encode function looks correct at first glance.
I also don't see an easy way to actually unit test this code as it is without actually running it through sqlite which outs a lot of dependencies on the unit test.
I would either need to spend a lot more time massaging gpt to get this to a point where I would be fine shipping the code or you know just write it myself.
Regarding your point around being able to whip up the code yourself - the point is to have a decent starting point to save time and energy. Like you said, you know the edge cases so you could skip the boring parts using GPT and focus purely on fixing those. Though, with more prompting (especially providing examples), GPT can also handle that for you.
I have nearly 2 decades of experience as a developer and it took me a while to reorient my flow around LLMs. But now that I have, it’s truly gamechanging.
And since you asked, here’s my system prompt:
You are an experienced developer who follows industry standards and best practices. Write lean code and explain briefly using bullet points or numbered lists. Elaborate only when explaining concepts or making choices. Always mention which file and where to store provided code.
Tech Stack: < insert all the languages, frameworks, etc you’d like to use >
If I provide code, highlight and explain problematic code. Also show and explain the corrected code.
Take a deep breath and think step by step.
Also, always use GPT4 and customize the above to your style and liking.
There is some code I would really prefer not to write that is a decent test case for this and won't expose company code to GPT. Will give feedback when I am done. Maybe you are correct.
It handles even more complex use cases and will automatically include/patch code for you via the inbuilt LLM framework. This helps with iteration and modifications as you massage it to what you need. Plus, it’ll scan your code and find the languages/frameworks automatically.
Finally, keep in mind that the goal should not be perfect production code - that’s just Twitter AI hype. It’s about saving time and energy for you (the human) to achieve more than possible before.
I tried your prompt and the above approach and it took me about 45 minutes of putsing around to get a result I am happy to begin iteration on.
Effectively: I have an 80bit byte array representing a timestamp struct consisting of a 48 bit unsigned integer for seconds and a 32 bit unsigned integer representing nanoseconds. The byte array is big endian and the host systen is little endian.
I gave it full signatures for all functions and relevant structs and instructions on how I would want the parsing done regarding algorithmic complexity and yet it still took multiple iterations to get anything useful.
At this point it is converting to little endian during the decode then doing a check if host the system is big endian and converting back to big endian if that is true.
There is likely some easy optimisations to be done where there and I would have definitely have gotten to this point quicker had I just written the 10 lines of code this needed and would have done the optimisations where I'm pretty sure that entire operation can happen in a few instructions.
> My guess is that this was generated using GPT4?
This is a good guess, since I stated outright that I used GPT-4, and then mentioned GPT-4 later on in the comment.
I asked it to try again and got a better result though, just didn't include it.
I'm grateful because I thought code LLMs only sped up the "RTFM" part, but it made me find those libs so I didn't have to Google around for (and sometimes it's hard to guess if they're the right tool for the job, and they might be behind in SEO).
1. Being the "senior developer" who spend their whole career working with a technology you're very junior at. No matter what you do and how long your programming career is, you're inevitably going to run into one of these sooner or later. Whether it's build scripts, frontend code, interfacing with third-party APIs or something else entirely, you aren't an expert at every technology you work with.
2. Writing the "boring" parts of your program, and every program has some of these. If you're writing a service to fooize a bar really efficiently, Copilot won't help you with the core bar fooization algorithm, but will make you a lot faster at coding up user authentication, rate limiting for different plans, billing in whatever obscure payment method your country uses etc.
3. Telling you what to even Google for. This is where raw Chat GPT comes into play, not Copilot. Let's say you need a sorting algorithm that preserves the order of equal elements from the original list. This is called stable sorting, and Googling for stable sorting is a good way to find what you're looking for, but Chat GPT is usually a better way to tell you what it's called based on the problem description.
Neither is the LLM.
Now microwaving eggs... that's a different matter.
That's funny. It makes me wonder how these statistical mad libs machines will handle the gradual boundaries nature gives us. Almost all mammals give birth live, but not all. Nearly all mammals had mammalian parents, but not all.
Daniel Dennett was making this argument for why we haven't developed reasonable models for the nature of consciousness. It's because we're so sure there will be an absolute classification, and not a gradual accumulation of interacting systems that together yield the phenomenon.
https://huggingface.co/SkunkworksAI/BakLLaVA-1
you'll have to brave 4chan yourself to find links to the NSFW ones, I don't actually have them.
you mean the LLava based variants ?
Side note: ChatGPT is now completely useless for most creative tasks. I'm trying to use it, via NovelCrafter, to help flesh out a story where a minor character committed suicide. ChatGPT refuses to respond, mentioning "self harm" as a reason.
The character in question killed himself before the story even begins (and for very good reasons, story-wise); it's not like one's asking about ways to commit suicide.
This is insane, ridiculous, and different from what all other actors of the industry do, including Claude or Mistral. It seems OpenAI is trying to shoot itself in the foot and doing a pretty good job at it.
Perhaps the HR support line for OpenAI developers tasked with implementing the censorship system?
It's funny how sometimes the quality of associate work drops as the topics make text generators less useful.
The way forward for corporations is almost certainly either for very specific use cases or local/internal LLMs. Producers of these will probably be less afraid of being canceled by populists, hence introduce less censorship.
> Seems like an unusual use case for most businesses.
You may not work in a public affairs consultancy. Companies do different things. Well drilling is also "an unusual use case for most companies". That does not make it any less important.
If your tool tries to fiddle with the content of your statement, it's not a serious tool. No one would accept their spell check tool to have an opinion on the type of content it is correcting.
* https://github.com/LostRuins/koboldcpp/releases/tag/v1.61.2
To a first order approximation, LLMs are bandwidth constrained. We can estimate single batch throughput as Memory Bandwidth / (Active Parameters * Parameter Size).
An 8-bit quantized Llama 2 70B conveniently uses 70GiB of VRAM (and then some, let's ignore that.) The M3 Max with 96GiB of VRAM and 300GiB/s bandwidth would have a peak throughput around 4.2 tokens per second.
Quantized models trade reduced quality for lower VRAM requirements and may also offer higher throughput with optimized kernels, largely as a consequence of transfering less data from VRAM into the GPU die for each parameter.
Mixture of Expert models reduce active parameters for higher throughput, but disk is still far too slow to page in layers.
In my case I got the runtime detection issue (explained in the README "gotcha" section). Solved my running "assimilate" [3] on the downloaded llamafile.
[1] https://github.com/Mozilla-Ocho/llamafile/
[2] https://huggingface.co/jartine/Mixtral-8x7B-Instruct-v0.1-llamafile/resolve/main/mixtral-8x7b-instruct-v0.1.Q5_K_M.llamafile?download=true
[3] https://cosmo.zip/pub/cosmos/bin/assimilateAnd privacy is a good enough reason to use local LLMs over commercial ones.
Totally. I recently asked a locally-run "speed" LLM for the best restaurants in my (major) city, but it spit out restaurants opened by chefs from said city in other cities. It's not a thing you'd want to rely on for important work, but is still quite something.
If you just want to play around with an LLM though, absolutely.
Between that and dirt cheap storage prices, it is possible to have a local, offline copy of more human knowledge than one can sensibly consume in a lifetime. Hell, it's possible to have it all on one's smartphone (just get one with an SD card slot and shove a 1+ Tb one in there).
> on an old raspberry pi
I bet the LLM responses will be great... You're better off just opening up a raw text dump of Wikipedia markup files in vim.
First there is a leaderboard for embeddings. [1]
Even then, it depends how you use them. Some embeddings pack the highest signal in the beginning so you can truncate the vector, while most can not. You might want that truncated version for a fast dirty index. Same with using multiple models of differing vector sizes for the same content.
Do you preprocess your text? There will be a model there. Likely the same model you would use to process the query.
There is a model for asking questions from context. Sometimes that is a different model. [2]
Are LLMs unable to distinguish between real life and fantasy? What prompts have you thrown at them to make this determination? Sending a small fairy tale and asking the LLM if it thinks it's a real story or fake one?
"Shit in, shit out" as the saying goes, but applied to conversations with LLMs where the prompts often aren't prescriptive enough.
There’s so much subtle knowledge like the way a mother learns to calm her child or the way a carpenter learns to work different kinds of wood which may be written down in part, but may also be learned through lived experience or transferred from human to human such that little of it gets written down and posted online.
I can't help but feel we're at the "aliens watch people eat from space and recreate chemically identical food that has no taste" phase of AI development.
If it were 99.9% chemically identical but they left out the salt and spices…
And so without the correct amount of salt and/or spices, we're talking about food that's very similar, and not identical.
* They might not perceive some substances at all, others that we don't notice might make it unpalatable.
* Some substances might be perceived differently than us, or be indistinguishable from others.
* And some might require getting used to.
Note that all of the above phenomena also occur in humans because of genetics, cultural background, or experiences!
However, there is a broader context: this is supporting an argument against physicalism, and in this light it falls apart. There are a couple of missing bits required to complete the experiment in this context. The understanding that knowledge comes in 2 varieties: direct (actual experience) and indirect (description by one with the actual experience using shared language). This understanding brings proper clarity to the original argument, as we are aware - I think - that language is used to create compressed representations of things; something like a perceptual hash function.
The other key bit, which I guess we've only considered and extensively explored after the argument was formulated, is that all information coming in via the senses goes to the brain as electrical signals. And we actually have experimental data showing that sensory information can be emulated using machines. Thus, the original argument, to be relevant to the context, should be completed by giving Mary access to a machine that she can program to emulate the electrical signals that represent color experience.
I posit that without access to that hypothetical machine, given the context of the experiment, it cannot be said that Mary has "learned everything there is to learn about color". And once she has comprehensively and correctly utilized said machine on herself, she will gain no new knowledge when she is exposed to the world of color. Therefore this experiment cannot be used as an argument against physicalism as originally intended.
All that took was words and a shared experience of smelling.
But, fair point. The gist I was trying to get across is that I don't even know what a plant smells like to you, and you don't know what a plant smells like to me. Those aren't comparable with any objective data. We make guesses, and we try to get close with our descriptions, which are in words. That's the best we can do and we share our senses. Asking more from computers seems overly picky to me.
From oral tradition to tablets to scrolls to books to mass produced books to digital and now these LLMs, I think it’s still a good idea to preserve what we have the best we can. Not as a replacement, but a hedge against a potential library of Alexandria incident.
I could imagine a time in the near future where the models are domain-specific, and just like there are trusted encyclopedia publishers there are trusted model publishers that guarantee a certain level of accuracy.
It’s not like reading a book, but I for sure had an easier time learning golang talking with ChatGPT than a book
What would cause a Library of Alexandria incident wiping out all human knowledge elsewhere, that would also allow you to run a local LLM?
What you're really asking is, what could cause a modern Library of Alexandria incident? But the fact is we keep the only copy of too many things on the servers of the major cloud providers. Which are then intended to have their own internal redundancy, but that doesn't protect you against a targeted attack or a systemic failure when all the copies are under the same roof and you lose every redundant copy at once from a single mistake replicated in a monoculture.
Or more Sci-fi related, some hyper computer virus that ends up infecting all internet connected devices.
Not too far fetched if we can conceive of some AI enabled worm that mutates depending on the target, I could imagine a model of sorts being feasible within the next 5-10 years
Venture capital in tech is a good example of this. The book knowledge is effectively globally distributed and almost free, effectively success happens in a few geographically concentrated counties.
Same for your example, no logical link between the effect and the consequences.
In general AI in 2024 is not even close to understanding these ideas, nor does any AI developer have a clue how to build an AI with this understanding. The best we can do is imitating object permanence for a small subset of perceptible objects, a limitation not found in dogs or spiders.
My worst case was with a guy who asked me to write an arbitrage betting bot. When I asked how to calculate coeffs, he pointed at two values and said "look, there <x>, there <y> thinks for a minute then it's <z>!". When I asked how exactly did he calculate it, he simply repeated with different numbers.
Now you know how an LLM feels during training!
Once upon a time I was good at a video game. Everyone realized that positioning is extremely important in this game.
I have good positioning in that game and was asked many times to make a guide about positioning. I never did, because I don't really know how. There is too much information they you need to convey to cover all the various situations.
I think you would first have to come up with a framework on positioning to be able to really teach this to someone else. Some kind of base truths/patterns that you can then use to convey the meaning. I believe the same thing applies to a lot of these processes that aren't verbalized.
Imagine someone learning English has written "The experiment reached it's conclusion" and you have to correct their grammar. Almost any english speaker can correct "it's" to "its" but unless they (and the person they're correcting) know a bunch of terms like 'noun' and 'pronoun' and 'possessive' they'll have a very hard time explaining why.
For example, I've never learned English, anywhere. I know it from .txt and .ng documents and a couple of dictionaries I had back in the DOS days. I'm an uneducated text-native, basically. But here's what I'd say to that newbie:
- Usually we use "...'s" for attribution like in "human's cat = cat of a human". But "it" and other special words like "that", "there", etc are an exception. We write "it's" as short for "it is", sometimes "it has". But we write "its", "theirs" for attribution, like in "its paw" = "paw of it" ~~ "cat's paw" = "paw of a cat". There's more to this, but you can ignore it for now.
Language is so good, that an artificial language tool, without any understanding of these mental processes, can appear semi-intelligent to us.
A few people unable to do this serialization doesn't mean much on the larger scale. Just that their ideas and mental processes will be forgotten.
Yes - the available training data is essentially mostly a combination of declarative knowledge (facts - including human-generated artifacts) and procedural knowledge (how to do things). What is missing is the learning process of taking a description of how to do something, and trying to apply that yourself in a specific situation.
No amount of reading books, or reading other people's blogs on how they did something, can avoid the need for hands-on experience if you want to learn how to do it yourself.
It's not just a matter of information that might be missing or unclear in instructional material, including how to cope with every type of failure and unexpected outcome, but crucially how to do this yourself - if you are to be the actor, then it's the predictive process in your mind that matters.
Partly for this reason, and partly because current AI's (transformer-based LLMs) don't support online learning (try & fail skill acquisition), I think we're going to see two distinct phases of AI.
1) The current "GenAI" phase where AI can only produce mash-ups of things it saw in it's pre-training data, augmented by similar "book learning" provided in-context which can be utilized by in-context learning. I'd characterize what this type of AI to be useful for, and capable of, as "automation". Applying that book (incl. anecdotal) knowledge to new situations where mash-up is all you need.
2) The second phase is where we have something closer to AGI, even if still below human level, which is no longer just a pre-trained transformer, but also has online learning and is agentic - taking actions predicated on innate traits like curiosity and boredom, so that given the book knowledge it can (& will!) then learn to apply that by experimentation/practice and learning from its own mistakes.
There will no doubt be advances beyond this "phase two" as well, but it seems we're likely to be stuck at "phase one" for a while (even as models become much better at phase one capabilities), until architectures fundamentally advance beyond transformers to allow this type of on-the-job training and skill acquisition.
It gave me a new mental model for LLMs rather than a "spicy autocomplete" or whatever, I now think of it as "a lossy compressed database of knowledge". Like you ran the internet through JPEG at 30% quality.
In this scenario you’d need to also be left with a big chunk of compute, and power infrastructure. Since ChatGPT is the front end of the model you’d also need to have the internet still going in a minimum capacity.
But really, didn't you catch the meaning of parents message, or are you being purposefully obtuse?
Given the way LLMs are right now, I suspect there will be lot of failed experiments and the kingdom of science will not advance that quick.
It’s more likely that it wouldn’t even start. The first step to any development was figuring out nitric acid as the cure to the petrification. Good luck getting any LLM to figure that out. Even if it did, good luck getting any of the other characters to know what to do with that information that early on.
Both the context window and output structure are, in my opinion, massive impedance mismatches for the emergent intellect embedded in the weights of the model.
If there were a way to match the impedance, I strongly suspect we'd already have AGI on our hands.
LLMs are of this earth and created by our species. Seems quite familiar to me.
The chat interfaces (Claude, ChatGPT) certainly have a particular style of writing, but the underlying LLMs are definitely capable of impersonating as our species in the medium of text.
I saw this talk with Geoffrey Hinton the other day and he said he was astonished at the capabilities of ChatGPT-4 because he asked it what the relationship between a compost heap and a nuclear bomb was, and he couldn't believe it answered, he really thought it was proof the thing could reason. Totally mind blown.
However I got it right away with zero effort.
Either I'm a super genius or this has been discussed before and made it's way into the training data.
Usual disclaimer: I don't think this invalidates the usefulness of AI or LLMs, just that we might be bamboozling ourselves into the idea that we've created an alien intelligence.
If an LLM can tell you the relatonship between a compost heap and nuclear bomb, that doesn't mean that was in the training data.
It could be because a compost heap "generates heat", and a nuclear bomb also "generates heat" and due to that relationship they have something in common. The model will pick up on these similar patterns. They tokens are positioned closer to each other in the high dimensional vector space.
But for any given "what does x have in common with y", that doesn't necessarily mean someone has asked that before and it's in the training data. Is that reasoning? I don't know ... how does the brain do it?
It's a lot of organic matmuls. ;)
That answer was close btw !
That's not familiar behavior. Nor is the counting reddit derived output. It's also not familiar for a single person to have the breadth and depth of knowledge that ChatGPT has. Sure, some people know more than others, but even without hitting the Internet, it has a ridiculous amount of knowledge, far surpassing a human, making it, to me, alien. though, it's inability to do math sometimes is humanizing to me for some reason.
ChatGPT's memory is also unhuman. It has a context window which is a thing, but also it only knows about things you've told it in each chat. Make a new chat and it's totally forgotten the nickname you gave it.
I don't think of HR Geiger's work, though made by a human, as familiar to me. it feels quite alien to me, and it's not just me, either. Dali, Bosch, and Escher are other human artists who's work can be unfamiliar and alien. So being created by our species doesn't automatically imbue something with familiar human processes.
So it dot products, it matrix multiplies, instead of reasoning and understanding. It's the Chinese room experiment on steroids; it turns out a sufficiently large corpus on a sufficiently large machine does make it look like something"understands".
"Alien" still works, but then you might have to add all the context at length, as you've done in this last comment.
AI researchers unveil alien intelligence
Math is mostly impeded by the tokenization, but it would still make more sense to adapt them to use RAG to process questions that are clearly calculations or chains of logical inference. With proper prompt engineering, they can process the latter though, and deviating from strictly logical reasoning is sometimes exactly what we want.
The ability to reset the text and to change that history is a powerful tool! It can make the model roleplay and even help circumvent alignment.
I think that LLMs could one day serve as the language center of an AGI.
Aliens are people from other countries, for example.
Exotic would be another good word to use.
This is a fundamental limitation, much more fundamental than appears at first. It means that the only way to touch the model, and for the model to touch the world, is through the tokenizer (also, btw, why tokenizer is so essential to model performance). Touching the world through a tokenizer is actually quite limited.
So there is an intelligence in there for sure, but it is locked in an ontology that is tied to its interface. This is even more of a limitation than e.g. weights being frozen.
> Australia won the 1987 Cricket World Cup. The 1986 date is incorrect; there was no Cricket World Cup in 1986. The tournament took place in 1987, and Australia defeated England in the final to win their first title.
https://chat.openai.com/share/e9360faa-1157-4806-80ea-563489...
I'm no cricket fan, so someone will have to correct Wikipedia if that's wrong.
If you want to point out that LLMs hallucinate, you might want to speak plainly and just come out and say it, or at least give a real world example and not one where it didn't.
>>> who won the 1986 Cricket World Cup
India
>>> who won the 1986 Cricket World Cup
Australia
>>> who won the 1986 Cricket World Cup
New Zealand
>>> who won the 1986 Cricket World Cup
West Indies
>>> who won the 1986 Cricket World Cup
England
> Brooklyn Nets won the 1986 Cricket World Cup by scoring 46 yards in only 3 frames
LLM's hallucinations are insidous because they have the ring of truth around them. yards and frames aren't cricket terms, so we're off to the races with them.
This makes sense. If you interact with a language model and it says something wrong it is your fault
It's like how you have to set x264 to the anime tuning or the film tuning depending on what you run it on.
Furthermore, even if an LLM always gives the same answer to a question, there’s no guarantee the answer is correct.
For fact retrieval you need to use temperature 0.
If you don't get the right facts then try 34b, 70b, Mixtral, Falcon 180b, or another highly ranked one that has come out recently like DBRX.
Then after all is indexed you can pursue RAG on a richer set of metadata. Though i've got no idea what that stateful world view is.
Then, when future messages come in from alternate streams (say, browser history), they could (maybe, hah) be made more rich. More likely though i would expect it to be the opposite scenario, browser informs chat, etc.
I say this because in many cases i imagine my chat conversations in my household have a severe lack of context. We often jump to vocal communication, and then paste links, etc. In a perfect world i think i'd even take home camera audio transcripts and do the same.
Ie i don't want to _just_ index a browser log as "interested in Rust. Some library about BTree", etcetc - but additional sources of data could try to store what it is i am actively doing, and associate that to the browser log.
All of this of course is nothing i'd ever want to leave the house. My hope though is that it would lean into what LLMs do well. Without the expectation of actual LLM intelligence.
They learn statistics on texts and are able to regurgitate them somewhat.
A year ago, a lot of this information only existed on the internet, and would have been nearly impossible to recover in any cohesive unfragmented form if the lights were to ever go out on our civilization.
Now the problem space has moved simply to "find a single solitary PC that will still boot up", and boom, you have access to everything.
I think we just created our Rosetta stone.
If you want a friendly but fallible UI to that dump, download an LLM and build a simple ReAct framework around it with prompting to use the wikipedia dump for reference.