Comment by thesz

19 days ago 

  > like graph relationships

Once upon a time during me being language modeling researcher I built and finetuned a big (at the time - about 5 billions parameters) Sparse Non-Negative Matrix Language Model [1].

[1] https://aclanthology.org/Q16-1024/

As this model allows for mix-and-match of various contexts, one thing that I did is to have a word-sorted context. This effectively transforms position-based context into a word-set based context, where "you and me", "me and you" and "and me you" are the same.

This allowed for longer contexts and better prediction.

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thesz

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nickpsecurity  19 days ago

I've saved it to look at it in the future. I also remembered Kristina Tautanova's name (your editor). Looking up recent publications, she's done interesting work on analyzing pretraining mixtures.

https://aclanthology.org/2025.acl-long.1564/

Thanks to you both for two, interesting papers tonight. :)

  • thesz  18 days ago

    I am not an author of SNMLM paper. ;)

    I was using their model in my work.

    • nickpsecurity  18 days ago

      I misunderstood what you said.

      Well, in your work, whay benefit did you get from it? And do you think it would be beneficial today combined with modern techniques? Or obsoleted by other technqiue?

      (I ask because I'm finding many old techniques are still good or could be mixed with deep learning.)

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