6 ms·

I have found the youtube videos by CodeEmporium to be simpler to follow https://www.youtube.com/watch?v=Nw_PJdmydZY https://www.youtube.com/watch?v=Nw_PJdmydZY

• blt 3y ago
  As a computer scientist, the "differentiable hash table" interpretation worked for me. The AIAYN paper alludes to it by using the query/key/value names, but doesn't explicitly say the words "hash table". I guess some other paper introduced them?
• nerdponx 3y ago
  > TBF there is no good explanation why it works My mental justification for attention has always been that the output of the transformer is a sequence of new token vectors such that each individual output token vector incorporates contextual information from the surrounding input token vectors. I know it's incomplete, but it's better than nothing at all.
  • rcarmo 3y ago
    You're effectively steering the predictions based on adjacent vectors (and precursors from the prompt). That mental model works fine.
  • eurekin 3y ago
    > TBF there is no good explanation why it works I thought the general consesus was: "transformers allow neural networks to have adaptive weights". As opposed to the previous architectures, were every edge connecting two neurons always has the same weight. EDIT: a good video, where it's actually explained better: https://youtu.be/OFS90-FX6pg?t=750&si=A_HrX1P3TEfFvLay https://youtu.be/OFS90-FX6pg?t=750&si=A_HrX1P3TEfFvLay
• mjburgess 3y ago
  The explanation is just that NNs are a stat fitting alg learning a conditional probability distribution, P(next_word|previous_words). Their weights are a model of this distribution. LLMs are a hardware innovation: they make it possible for GPUs to compute this at scale across TBs of data. Why does, 'mat' follow from 'the cat sat on the ...' because 'mat' is the most frequent word in the dataset; and the NN is a model of those frequencies. Why is 'London in UK' "known" but 'London in France' isnt? Just because 'UK' much more frequently occurs in the dataset. The algorithm isnt doing anything other than aligning computation to hardware; the computation isnt doing anything interesting. The value comes from the conditional probability structure in the data. -- that comes from people arranging words usefully, because they're communicating information with one another
  • sirsinsalot 3y ago
    It isn't some kind of Markov chain situation. Attention cross-links the abstract meaning of words, subtle implications based on context and so on. So, "mat" follows "the cat sat on the" where we understand the entire worldview of the dataset used for training; not just the next-word probability based on one or more previous words ... it's based on all previous meaning probability, and those meaning probablility and so on.
  • fellendrone 3y ago
    > Why does, 'mat' follow from 'the cat sat on the ...' You're confidently incorrect by oversimplifying all LLMs to a base model performing a completion from a trivial context of 5 words. This is tantamount to a straw man. Not only do few people use untuned base models, it completely ignores in-context learning that allows the model to build complex semantic structures from the relationships learnt from its training data. Unlike base models, instruct and chat fine-tuning teaches models to 'reason' (or rather, perform semantic calculations in abstract latent spaces) with their "conditional probability structure", as you call it, to varying extents. The model must learn to use its 'facts', understand semantics, and perform abstractions in order to follow arbitrary instructions. You're also confabulating the training metric of "predicting tokens" with the mechanisms required to satisfy this metric for complex instructions. It's like saying "animals are just performing survival of the fittest". While technically correct, complex behaviours evolve to satisfy this 'survival' metric. You could argue they're "just stitching together phrases", but then you would be varying degrees of wrong: For one, this assumes phrases are compressed into semantically addressable units, which is already a form of abstraction ripe for allowing reasoning beyond 'stochastic parroting'. For two, it's well known that the first layers perform basic structural analysis such as grammar, and later layers perform increasing levels of abstract processing. For three, it shows a lack of understanding in how transformers perform semantic computation in-context from the relationships learnt by the feed-forward layers. If you're genuinely interested in understanding the computation model of transformers and how attention can perform semantic computation, take a look here: https://srush.github.io/raspy/ https://srush.github.io/raspy/ For a practical example of 'understanding' (to use the term loosely), give an instruct/chat tuned model the text of an article and ask it something like "What questions should this article answer, but doesn't?" This requires not just extracting phrases from a source, but understanding the context of the article on several levels, then reasoning about what the context is not asserting. Even comparatively simple 4x7B MoE models are able to do this effectively.