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Comment by zxexz | Hacker News Reader

I've seen some very impressive results just embedding a pre-trained KGE model into a transformer model, and letting it "learn" to query it (I've just used heterogenous loss functions during training with "classifier dimensions" that determine whether to greedily sample from the KGE sidecar, I'm sure there are much better ways of doing this.). This is just subjective viewpoint obviously, but I've played around quite a lot with this idea, and it's very easy to get a an "interactive" small LLM with stable results doing such a thing, the only problem I've found is _updating_ the knowledge cheaply without partially retraining the LLM itself. For small, domain-specific models this isn't really an issue though - for personal projects I just use a couple 3090s.

I think this stuff will become a lot more fascinating after transformers have bottomed out on their hype curve and become a tool when building specific types of models.

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> embedding a pre-trained KGE model into a transformer model

Do you have any good pointers (literature, code etc) on the mechanics of this?

Check out PyKEEN [0] and go wild. I like to train a bunch of random models and "overfit" them to the extreme (in my mind overfitting them is the point for this task, you want dense, compressed knowledge). Resize the input and output embeddings of an existing pretrained (but small) LLM (input only necessary if you're adding extra metadata on input, but make sure you untie input/output weights). You can add a linear layer extension to the transformer blocks, pass it up as some sort of residual, etc. - honestly just find a way to shove it in, detach the KGE from the computation graph and add something learnable between it and wherever you're connecting it - like just a couple linear layers and a ReLU. The output side is more important, you can have some indicator logit(s) to determine whether to "read" from the detached graph or sample the outputs of the LLM. Or just always do both and interpret it.

(like tinyllama or smaller, or just use whatever karpathy repo is most fun at the moment and train some gpt2 equivalent)

[0] https://pykeen.readthedocs.io/en/stable/index.html

Sorry if that was ridiculously vague. I don't know a ton about the state of the art, and I'm really not sure there is one - the papers just seem to get more terminology-dense and the research mostly just seems to end up developing new terminology. My grug-brained philosophy is just to make models small enough you can just shove things in and iterate quick enough in colab or a locally hosted notebook with access to a couple 3090s, or even just modern Ryzen/EPYC cores. I like to "evaluate" the raw model using pyro-ppl to do MCMC or SVI on the raw logits on a known holdout dataset.

Really always happy to chat about this stuff, with anybody. Would love to explore ideas here, it's a fun hobby, and we're living in a golden age of open-source structured datasets. I haven't actually found a community interested specifically in static knowledge injection. Email in profile, in (ebg_13 encoded).

Thank you for your comments (good further reading terms), and your open invitation for continued inquiry.

The "fomo" / deja vu / impending doom / incipient shift in the Overton window regarding meta-architecture for AI/ML capabilities and risks is so now glaring obvious of an elephant in the room it is nearly catatonic to some.

https://www.youtube.com/watch?v=2ziuPUeewK0

We also did something similar in our NTULM paper at Twitter https://youtu.be/BjAmQjs0sZk?si=PBQyEGBx1MSkeUpX

Used in non generative language models like BERT but should help with generative models as well.

Thanks for sharing! I'll give it a read tomorrow - I do not appear to have read this. I really do wish there were good places for randos like me to discuss this stuff casually. I'm in so many slack, discord, etc. channels but none of them have the same intensity and hyperfocus as certain IRC channels of yore.

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