The 'Algorithmic Camp' perspective posits that Large Language Models learn to execute algorithms during pre-training and subsequently execute those algorithms for different tasks during in-context learning inference, as argued by Li et al. (2023a), Zhang et al. (2023), and Bai et al. (2023b).

Authors

• Person: Not available Organization: arXiv
  A Survey on the Theory and Mechanism of Large Language Models

Sources

• A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv via serper

Referenced by nodes (2)

• Large Language Models concept
• Pre-training concept