ALiBi uses relative positional encoding, avoiding fixed-size embeddings, enabling better handling of variable-length sequences
Image: Cmichel67, CC BY-SA 4.0, via Wikimedia Commons
ALiBi uses relative positional encoding, avoiding fixed-size embeddings, enabling better handling of variable-length sequences
cosine similarity is preferred over dot product for normalized embeddings
Why do we need a special way to measure similarity in high-dimensional spaces?
rotary position embeddings (RoPE) do
RoPE encodes relative position by applying rotation matrices to input features
weight tying does in language models: shares embedding and output projection matrices
Ever wonder how machines understand the sequence of words in a sentence?
768-dim BERT embeddings capture: bidirectional context from masked language modeling
768-dim BERT embeddings capture bidirectional context from masked language modeling
List of algorithms
Cosine similarity measures the angle between vectors, not their magnitude
batch size affects generalization: larger batches find sharper minima
Larger batch sizes lead to sharper minima, enhancing generalization by providing more accurate gradient estimates
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