  ORCA (Shen et al., 2023) is a recent technique for cross-modal fine-tuning,
i.e., applying pre-trained transformer models to modalities beyond their
training data. The technique consists primarily of training an embedder and
fine-tuning the embedder and model. Despite its high performance on a variety
of downstream tasks, we do not understand precisely how each of these
components contribute to ORCA's success. Therefore, we run a series of
ablations and find that embedder training does not help 2D tasks at all,
contrary to what the original paper posits. In 1D tasks, some amount of
embedder training is necessary but more is not better. In 4 out of 6 datasets
we experiment with, it is model fine-tuning that makes the biggest difference.
Through our ablations and baselines, we contribute a better understanding of
the individual components of ORCA.
