Train Once, Answer All: Many Pretraining Experiments for the Cost of One

Documenti analoghi

• Promote, Suppress, Iterate: How Language Models Answer One-to-Many Factual Queries
  di: Yan, Tianyi Lorena, et al.
  Pubblicazione: (2025)
• How Much Can We Forget about Data Contamination?
  di: Bordt, Sebastian, et al.
  Pubblicazione: (2024)
• Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
  di: Bordt, Sebastian, et al.
  Pubblicazione: (2024)
• Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?
  di: Méloux, Maxime, et al.
  Pubblicazione: (2025)
• Many-to-English Machine Translation Tools, Data, and Pretrained Models
  di: Gowda, Thamme, et al.
  Pubblicazione: (2021)