From Words to Numbers: Your Large Language Model Is Secretly A Capable
  Regressor When Given In-Context Examples
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.
661889cca7c93dc43f605f20
Robert Vacareanu
claimed_verified
2024-04-14T00:51:04.229Z
626218b70568e418d6949682
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Robert Vacareanu
user
robertvacareanu
661889cca7c93dc43f605f21
Vlad-Andrei Negru
661889cca7c93dc43f605f22
Vasile Suciu
661889cca7c93dc43f605f23
Mihai Surdeanu
From Words to Numbers: Your Large Language Model Is Secretly A Capable
  Regressor When Given In-Context Examples
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.
Large language models can perform regression tasks competitively with traditional supervised methods and achieve sub-linear regret with increasing in-context examples.
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