How Far Apart Does a Model Think Its Tokens Are? (opens in new tab)

Instead of using static position increments (+1) per token, RoPE-based language models can learn per-token and per-layer position increments. This has no detectable effect on model performance but allows us to see what the model thinks the distance is between each position and how this varies per-layer.Example sentence with each character plotted based on per-layer learned position increments. Note the clear punctuation-based boundaries in L0 and what looks like concept-based grouping in L3.I...