Adding the position vector is basic sure, but it's naive to think the model doesn't develop its own positional system bootstrapping on top of the barebones one.

For some reason people are still adding position encodings into embeddings.

As if they are not relying on the model's ability to develop its own "positional system bootstrapping on top of the barebones one."

  > 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.

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. :)

I am not an author of SNMLM paper. ;)

I was using their model in my work.

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.)

At the time (2018), it had perplexity close to LSTM, while having more coefficients and much shorter (hours vs days) training time.

I tried to apply SNMLM's ideas to the byte-level prediction modeling here: https://github.com/thesz/snmlm-per-byte

It was not bad, but I had trouble scaling it to the 1B set. Mostly because I have not enough time.

I do hold same mindset as yours, that many old techniques are misunderstood or underapplied. For example, decision trees, in my experiments, allow for bit-length-per-byte comparable to LSTM (lstm-compress or LSTM in nncp experiments): https://github.com/thesz/codeta

> This is obviously not powerful enough to express non-linear relationships - like graph relationships.

the distance metrics used is based on energy-informed graphs that encode energy relations in a distribution called taumode, see my previous paper on spectral indexing for vector databases for a complete roll-out