Content-based privilege: transformer residual streams stratify by proximity to the model's own prediction

A new research paper suggests that the internal geometry of transformer models is organized by how close data is to the model's own prediction. As reported by LessWrong, researcher Nelson Guda found that this stratification affects how models discriminate between prompts and influences their behavior over time. The findings, which were consistent across eighteen different models, indicate that the directions nearest a prediction determine the immediate answer, while subsequent layers influence tokens appearing several steps later. Guda has released his code to the community to determine if these patterns are universal across all transformer architectures.

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