Researcher profile

Federico Barbero

1 paper in the PaperMetrix corpus

Publications

Papers by this author

  1. Round and Round We Go! What makes Rotary Positional Encodings useful?

    2024 · arXiv (Cornell University)

    Positional Encodings (PEs) are a critical component of Transformer-based Large Language Models (LLMs), providing the attention mechanism with important sequence-position information. One of the most popular types of encoding used today in LLMs are Rotary …