From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples
arxiv(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要