V. Torvik

Whether large language models can infer advising relationships from publication metadata alone and whether they can improve on a locally trained probabilistic ranking model, Model M are evaluated at two levels: paper-level advisor identification from a single publication and profile-level ranking and reranking of advisor candidates generated by Model M using the advisee’s first ten publications.