Transformers - powered by self-attention mechanisms - have been a paradigm shift in NLP, and are now the standard choice for training large language models. However, while transformers do have many benefits in terms of computational constraints - most saliently, that attention between tokens can be computed in parallel, rather than needing to be evaluated sequentially like in a RNN - a major downside is their memory (and, secondarily, computational) requirements. The baseline form of self-attention works by having every token attend to every other token, where "attend" here means that a query from each token A will take an inner product with each other token -A, and then be elementwise-multiplied with the values of every other token -A. This implies a O(N^2) memory and computation requirement, where N is your sequence length. So, the question this paper asks is: how do you get the benefits, or most of the benefits, of a full-attention network, while reducing the number of other tokens each token attends to. The authors' solution - Big Bird - has three components. First, they approach the problem of approximating the global graph as a graph theory problem, where each token attending to every other is "fully connected," and the goal is to try to sparsify the graph in a way that keeps shortest path between any two nodes low. They use the fact that in an Erdos-Renyi graph - where very edge is simply chosen to be on or off with some fixed probability - the shortest path is known to be logN. In the context of aggregating information about a sequence, a short path between nodes means that the number of iterations, or layers, that it will take for information about any given node A to be part of the "receptive field" (so to speak) of node B, will be correspondingly short. Based on this, they propose having the foundation of their sparsified attention mechanism be simply a random graph, where each node attends to each other with probability k/N, where k is a tunable hyperparameter representing how many nodes each other node attends to on average. To supplement, the authors further note that sequence tasks of interest - particularly language - are very local in their information structure, and, while it's important to understand the global context of the full sequence, tokens close to a given token are most likely to be useful in constructing a representation of it. Given this, they propose supplementing their random-graph attention with a block diagonal attention, where each token attends to w/2 tokens prior to and subsequent to itself. (Where, again, w is a tunable hyperparameter) However, the authors find that these components aren't enough, and so they add a final component: having some small set of tokens that attend to all tokens, and are attended to by all tokens. This allows them to theoretically prove that Big Bird can approximate full sequences, and is a universal Turing machine, both of which are true for full Transformers. I didn't follow the details of the proof, but, intuitively, my reading of this is that having a small number of these global tokens basically acts as a shortcut way for information to get between tokens in the sequence - if information is globally valuable, it can be "written" to one of these global aggregator nodes, and then all tokens will be able to "read" it from there. The authors do note that while their sparse model approximates the full transformer well in many settings, there are some problems - like needing to find the token in the sequence that a given token is farthest from in vector space - that a full attention mechanism could solve easily (since it directly calculates all pairwise comparisons) but that a sparse attention mechanism would require many layers to calculate. Empirically, Big Bird ETC (a version which adds on additional tokens for the global aggregators, rather than making existing tokens serve thhttps://i.imgur.com/ks86OgJ.pnge purpose) performs the best on a big language model training objective, has comparable performance to existing models on questionhttps://i.imgur.com/x0BdamC.png answering, and pretty dramatic performance improvements in document summarization. It makes sense for summarization to be a place where this model in particular shines, because it's explicitly designed to be able to integrate information from very large contexts (albeit in a randomly sampled way), where full-attention architectures must, for reasons of memory limitation, do some variant of a sliding window approach.