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This will still hallucinate, right? Projects like this for using with your documents datasets are invaluable, but everything I've tried so far is hallucinating, so not practical. What's the state of the art of the LLM without hallucination at the moment?
Earlier quoted context omitted.
If you mean the "If you don't know" part, oh no, they have a much bigger problem they're solving. The LLM will absolutely lie if it doesn't know and you haven't made it perfectly clear that you'd rather it did not do that. LLMs seem to be trying to give answers that make you happy. A good lie will make you happy. Unless it understands that you will not be happy with a lie. Is this anthropomorphizing? Yep. But that's…
A less anthropomorphic approach might be to say that LLMs can predict the correct “shape” of an answer even when they don’t have data that gives them a clear right answer for the correct content, and since their basic design is to provide the best response they can, they’ll provide an answer of the correct shape with fairly random content if all they have good information to predict is the shape and not the content.
How LLMs are able to give convincing wrong answers: they “can predict the correct ‘shape’ of an answer” (parent).
Why LLMs are able to give convincing wrong answers is a little more complicated, but basically it’s because the model is tuned by human feedback. The reinforcement learning from human feedback (RLHF) that is used to tune LLM products like ChatGPT is a system based on human ranking. It’s a matter of getting exactly what you ask for.
If you tune a model by having humans rank the outputs, despite your best efforts to instruct the humans to be dispassionate and select which outputs are most convincing/best/most informative, I think what you’ll get is a bias towards answers humans like. Not every human will know every answer, so sometimes they’ll select one that’s wrong but likable. And that’s what’s used to tune the model.
You might be able to improve this with curated training data (maybe something a little more robust than having graders grade each other). I don’t know if it’s entirely fixable though.
The brilliant thing about the parent’s comment about the “shape” of the answer is that it reveals how much humans have (uh, historically, now, I guess) relied on the shape of information to convey its trustworthiness. Expand the notion of “shape” a bit to include the medium. If somebody bothered to take the time to correctly shape an answer, we take that as a sign of trustworthiness, like how you might trust something written in a carefully-typeset book more than this comment.
Surely no one would take the time to write a whole book on a topic they know nothing about. Implies books are trustworthy. Look at all the effort that went in. Proof of effort. When perfectly-shaped answers in exactly the form you expected are presented in a friendly way and commercial context, they certainly read as trustworthy as Campbell’s soup cans. But LLMs can generate books worth of nonsense in exactly the right shapes without effort, so we as readers can no longer use the shape of an answer to hint at its trustworthiness.
So maybe the answer is just to train on books only, because they are the highest quality source of training data. And carefully select and accredit the tuning data, so the model only knows the truth. It’s a data problem, not a model problem
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
sentry-sdk 1.22.2 requires urllib3…many many lines
raise ValueError("%r is not a directory" % (package_path,))
ValueError: 'build/py3k/scipy' is not a directory
Converting to Python3 via 2to3... /tmp/pip-install-hkb_4lh7/scipy_088b20410aca4f0cbcddeac86ac7b7b1/build/py3k/scipy/signal/fir_filter_design.py
[end of output]
note: This error originates from a subprocess, and is
likely not a problem with pip.
error: metadata-generation-failed
Someone is thinking "this is part of learning the language," but I think it's just bad design.
does this only work with llamaCPP ? I.e. can't use GPU models with this?
Granted I'm not coming from the python world, but I have tried many of these projects, and very few of them install out of the box. They usually end with some incompatibility, and files scattered all over the place, leading to future nightmares. ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.…
python3 -m venv venv
. venv/bin/activate
Also, `python` is usually Python 2.7. If it is, I advise removing it from your system unless you have a strong reason to keep it.
With so many LLM options out there, how do we keep track of which ones are good?
While consumers are happy to get their data mined to avoid paying, businesses are the opposite: willing to pay a lot to avoid feeding data to MSFT/GOOG/META.
They may give assurances on data protection (even here GitHub copilot TOS has sketchy language around saving down derived data), but can’t get around fundamental problem that their products need user interactions to work well.
So it seems with BigTechLLM there’s inherent tension between product competitiveness and data privacy, which makes them incompatible with enterprise.
Biz ideas along these lines: - Help enterprises set up, train, maintain own customized LLMs - Security, compliance, monitoring tools - Help AI startups get compliant with enterprise security - Fine tuning service