Simply, it's when your output embedding matrix = input.
You save vocab_dim*model_dim params (ex. 617m for GPT-3).
But the residual stream means that the weight matrices are roughly connected via a matmul, which means they struggle to encode bigrams (commutative property enforces symmetry).
Attention + MLP adds nonlinearity, but it still means less expressivity.
Which is why they aren't SOTA, but are useful in smaller models.
The pragmatic tradeoff of tied embeddings · HackerTrans
You save vocab_dim*model_dim params (ex. 617m for GPT-3).
But the residual stream means that the weight matrices are roughly connected via a matmul, which means they struggle to encode bigrams (commutative property enforces symmetry).
Attention + MLP adds nonlinearity, but it still means less expressivity.
Which is why they aren't SOTA, but are useful in smaller models.