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I'm still very much learning this stuff, but I wonder if that's related to the vanishing gradient problem, which seems to be a fundamental aspect of these types

I'm still very much learning this stuff, but I wonder if that's related to the vanishing gradient problem, which seems to be a fundamental aspect of these types of approaches. (Please don't assume that's correct) https://en.wikipedia.org/wiki/Vanishing_gradient_problem https://en.wikipedia.org/wiki/Vanishing_gradient_problem

visarga 3y ago
Vanishing gradient was an issue for non-residual deep networks and vanilla RNNs. While the long context memory issues are along sequence dimension, not network depth. The problem could be some kind of instability of attention as it scales above 10k tokens. A recent paper suggests attention mechanism needs a default value (a "sink"), and its absence produces instability. https://arxiv.org/abs/2309.17453 https://arxiv.org/abs/2309.17453 Another paper says the middle part is lossy while the beginning and end are better attended.
- kristopolous 3y ago
  That's a really recent paper. Do you actually keep up to date with everything? How do you find the time?
  - totoglazer 3y ago
    Not to denigrate the person you’re responding to, but to add some context: That paper got a decent amount of attention already. Probably one of the more notable in the literature over the last month. Plus compared to the past year everything is slow now.
  - visarga 3y ago
    Just reading a couple papers every day, the most interesting ones, and following up on reddit and twitter to get notified what people are talking about. And I am directly interested in long-context LLMs for a work related task. I have also been dabbling with neural nets (pre-transformer), especially LSTM which have a "residual" connection, the one I was mentioning. That makes gradients better behaved. Schmidhuber tech.
- sandkoan 3y ago
  For anyone who's curious, the paper in question, entitled, "Lost in the Middle: How Language Models Use Long Contexts" (https://arxiv.org/abs/2307.03172 https://arxiv.org/abs/2307.03172)
amelius 3y ago
Regarding the vanishing gradient problem, has anyone tried to train using only a randomly chosen set of independent parameters in each iteration? (Updating only the weights in a small random independent set).
- jdthedisciple 3y ago
  Are you referring to Regularization? https://www.kaggle.com/code/sid321axn/regularization-techniques-in-deep-learning https://www.kaggle.com/code/sid321axn/regularization-techniq...

visarga 3y ago

Vanishing gradient was an issue for non-residual deep networks and vanilla RNNs. While the long context memory issues are along sequence dimension, not network depth. The problem could be some kind of instability of attention as it scales above 10k tokens. A recent paper suggests attention mechanism needs a default value (a "sink"), and its absence produces instability. https://arxiv.org/abs/2309.17453 https://arxiv.org/abs/2309.17453 Another paper says the middle part is lossy while the beginning and end are better attended.

kristopolous 3y ago
That's a really recent paper. Do you actually keep up to date with everything? How do you find the time?
- totoglazer 3y ago
  Not to denigrate the person you’re responding to, but to add some context: That paper got a decent amount of attention already. Probably one of the more notable in the literature over the last month. Plus compared to the past year everything is slow now.
- visarga 3y ago
  Just reading a couple papers every day, the most interesting ones, and following up on reddit and twitter to get notified what people are talking about. And I am directly interested in long-context LLMs for a work related task. I have also been dabbling with neural nets (pre-transformer), especially LSTM which have a "residual" connection, the one I was mentioning. That makes gradients better behaved. Schmidhuber tech.
sandkoan 3y ago
For anyone who's curious, the paper in question, entitled, "Lost in the Middle: How Language Models Use Long Contexts" (https://arxiv.org/abs/2307.03172 https://arxiv.org/abs/2307.03172)

amelius 3y ago

Regarding the vanishing gradient problem, has anyone tried to train using only a randomly chosen set of independent parameters in each iteration? (Updating only the weights in a small random independent set).

jdthedisciple 3y ago
Are you referring to Regularization? https://www.kaggle.com/code/sid321axn/regularization-techniques-in-deep-learning https://www.kaggle.com/code/sid321axn/regularization-techniq...