Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition

Documenti analoghi

• From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data
  di: Xiong, Zheyang, et al.
  Pubblicazione: (2024)
• Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
  di: Park, Jongho, et al.
  Pubblicazione: (2024)
• Extrapolation by Association: Length Generalization Transfer in Transformers
  di: Cai, Ziyang, et al.
  Pubblicazione: (2025)
• Pruning Everything, Everywhere, All at Once
  di: Nascimento, Gustavo Henrique do, et al.
  Pubblicazione: (2025)
• How Well Can Transformers Emulate In-context Newton's Method?
  di: Giannou, Angeliki, et al.
  Pubblicazione: (2024)