Summary

Researchers identify a phenomenon termed ‘massive activations’, exceptionally large activation values within LLMs, affecting attention mechanisms and output. The paper examines their widespread presence and implications for model biases and performance.

Highlights:

• Identification of Massive Activations: Significantly large activation values in LLMs.
• Role as Implicit Bias: Impacting LLM decisions, regardless of input.
• Influence on Attention Probabilities: Affects the advanced self-attention mechanisms in Transformer models.

Understanding massive activations is key in improving LLM designs for enhanced attention and decision-making mechanisms. This insight opens new avenues for research on model refinement and verification.