  We analyze how well pre-trained large language models (e.g., Llama2, GPT-4,
Claude 3, etc) can do linear and non-linear regression when given in-context
examples, without any additional training or gradient updates. Our findings
reveal that several large language models (e.g., GPT-4, Claude 3) are able to
perform regression tasks with a performance rivaling (or even outperforming)
that of traditional supervised methods such as Random Forest, Bagging, or
Gradient Boosting. For example, on the challenging Friedman #2 regression
dataset, Claude 3 outperforms many supervised methods such as AdaBoost, SVM,
Random Forest, KNN, or Gradient Boosting. We then investigate how well the
performance of large language models scales with the number of in-context
exemplars. We borrow from the notion of regret from online learning and
empirically show that LLMs are capable of obtaining a sub-linear regret.
