Decoding the Decoder LLM without de code: Ishan Anand
Aug 9, 2024 · 17:08
Ishan Anand demonstrates how decoder-based LLMs like GPT-2 work internally using a fully functional spreadsheet implementation. He explains tokenization (e.g., 're-injury' splitting into 'rain' and 'injury' due to subword units), 768-dimensional embeddings, multi-headed attention where tokens look backward (e.g., 'he' focuses 0.48 on 'Mike'), and the multi-layer perceptron with matrix multiplies (MMULT). He shows residual connections creating an information superhighway and uses logit lens to reveal layer-by-layer predictions (e.g., 'Wednesday' appears early but only rises to top probability at later layers). Finally, he injects a sparse autoencoder feature vector for 'Jedi' into the residual stream, steering GPT-2 to output 'lightsaber' instead of 'phone', and contrasts this with alternative steering methods like representation engineering and activation steering.
