Using Small Language Models to Reverse-Engineer Machine Learning Pipelines Structures
The study tested if Small Language Models (SLMs) could accurately extract Machine Learning pipeline structures from code. While SLMs showed promise, their performance was highly sensitive to how the classification terms were worded in the taxonomy. Although they performed competitively, they still suffered from high inference costs and lacked accuracy compared to existing model-based classifiers.