Scaling Transformers to 1B Tokens
The benefit of "traditional" O(N^2) transformer attention is you correlate every token to every other token. So, in the limit, your network won't "miss" much.<p>When you abandon O(N^2) attention, you are forced to start adding heuristics to choose what to correlate. Any time you see one of those giant context window LLMs, you need to be asking what heuristics they added, what is getting correlated, and what is <i>not</i> getting correlated.<p>This paper chooses an exponential heuristic where tokens further in the past get exponentially less attention. This heuristic is fine for certain tasks like responding in a chat room, where the most recent tokens are the most important, but bad for tasks where tokens are roughly equally important throughout the text, such as a dense academic paper or a reference manual.<p>The bitter lesson [1] is going to eventually come for all of these. Eventually we'll figure out how to machine-learn the heuristic rather than hard code it. Recurrent neural networks (RNNs) do this implicitly, but we don't yet know how to effectively train RNNs on ultra-deep sequences.<p>Another possibility is learning a heuristic for non-recurrent LLMs via reinforcement learning, such as in [2], which is basically a reinforcement learned "auto-researcher" that was trained in a style reminiscent of AlphaGo.<p>[1] <a href="http://www.incompleteideas.net/IncIdeas/BitterLesson.html" rel="nofollow noreferrer">http://www.incompleteideas.net/IncIdeas/BitterLesson.html</a><p>[2] <a href="https://arxiv.org/pdf/2109.00527.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/2109.00527.pdf</a>