The study also explores how the performance of these large language models scales with the number of in-context examples. Drawing from the concept of regret in online learning, the research empirically demonstrates that large language models can achieve a sub-linear regret, indicating their potential efficiency in learning from a growing number of examples.
Key takeaways:
- The study analyzes the performance of pre-trained large language models like Llama2, GPT-4, Claude 3, etc., in linear and non-linear regression tasks using in-context examples.
- Several large language models, such as GPT-4 and Claude 3, are found to perform regression tasks with a performance that rivals or even outperforms traditional supervised methods like Random Forest, Bagging, or Gradient Boosting.
- On the challenging Friedman #2 regression dataset, Claude 3 outperformed many supervised methods such as AdaBoost, SVM, Random Forest, KNN, or Gradient Boosting.
- The study also investigates how the performance of large language models scales with the number of in-context exemplars, showing that these models are capable of obtaining a sub-linear regret.