title: From Words to Numbers: Your Large Language Model Is Secretly A Capable
    Regressor When Given In-Context Examples
summary: 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.
paper_authors._id: 661889cca7c93dc43f605f20
paper_authors.name: Robert Vacareanu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-14T00:51:04.229Z
paper_authors._id: 626218b70568e418d6949682
paper_authors.avatarUrl: /avatars/79e063776120fe489f6157bd7e4ed37a.svg
paper_authors.fullname: Robert Vacareanu
paper_authors.type: user
paper_authors.user: robertvacareanu
paper_authors._id: 661889cca7c93dc43f605f21
paper_authors.name: Vlad-Andrei Negru
paper_authors._id: 661889cca7c93dc43f605f22
paper_authors.name: Vasile Suciu
paper_authors._id: 661889cca7c93dc43f605f23
paper_authors.name: Mihai Surdeanu
paper_title: From Words to Numbers: Your Large Language Model Is Secretly A Capable
    Regressor When Given In-Context Examples
paper_summary: 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.
paper_ai_summary: Large language models can perform regression tasks competitively with traditional supervised methods and achieve sub-linear regret with increasing in-context examples.
paper_ai_keywords: pre-trained large language models linear regression non-linear regression in-context examples supervised methods Random Forest Bagging Gradient Boosting Friedman #2 regression dataset AdaBoost SVM KNN sub-linear regret online learning