79.17±0.00
107.67±0.00
84.23±14.84

75.34±0.00
80.61±0.00
73.67±2.51

Random

ASKL

• LLM-ZS directly requests LLM to generate a zero-shot solution based solely on the task descrip-
tion. This approach is similar to using tools such as GitHub Copilot3 or Amazon CodeWhisperer4.
• LLM-FS uses the few-shot prompt technique [6] by adding some demonstrations to the prompt
to enable in-context learning. The demonstrations are randomly selected from our canonicalized
experience pool. Unlike MLCopilot, LLM-FS does not have access to advanced techniques such
as experience and knowledge retrieval.

5.4 Main Results
MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks