Show HN: LLM Attention Visualization

In one line

A visualization of the attention mechanism in LLMs.

Opening excerpt

One interesting thing about transformer-based large language models are that, during the generation phase, it is able to draw information from any of its previous tokens. But it needs to be selective; if every token affects the generation equally, it won’t be very effective. This process needs a mechanism to decide how much a token affects the next token.

You can tap or hover over any of the generated tokens to see the past tokens that affected* the generation.

• “Affected” might not be fully accurate, as this visualization is highly simplified. It’s calculating the attention weight, scaled by the magnitude of the value vector, aggregated across all attention heads, and summed across all layers. This is then used to control the opacity of the previous tokens. The largest values always have an opacity of 1 and the rest are interpolated.

A lot of information had to be thrown away to limit the visualization to just one numeric value per past token. Because of that, when I started implementing this, I actually thought it might not be comprehensible.

(Excerpted from the original; full article via the source link below.)

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