carefully designed [12, 35], the design of meta-features for complex tasks in PD1 is non-trivial. In
contrast, the text embedding approach has the additional advantage of not requiring any manual design
for new types of tasks. Furthermore, the text embedding method is more promising for handling new
and varied types of tasks, while the meta-feature for new task types is not easily scalable.
Nevertheless, we would like to emphasize that the key factor is not solely the method of retrieving
experience, but rather how the retrieved experience is utilized. When the retrieved experience is used
directly, as done in ASKL, all retrieval strategies perform poorly. In contrast, MLCopilot has the
ability to not only retrieve relevant experience, but also provide guidance through elicited knowledge
and leverage the power of LLMs.
Study of canonicalization. As shown in Table 7, the performance suffers considerably without
MLCopilot- Unleashing the Power of Large Language Models in Solving Machine Learning Tasks