Thank you for this fantastic method which fills the blank in group-level integration. I suggest the method uses group-pratition information rather than group annotation information. Honestly, like your article mentioned that sometimes the skewed annotation may get flawed result. But may it still helpful to add the annotation information as prior?
Practically, instead of hard annotation, LLM can be used to convert annotation to embeddings and then contributes to mapping matrix with prior-weights.
Do you think it's worth to be included into the packages?
Thank you for this fantastic method which fills the blank in group-level integration. I suggest the method uses group-pratition information rather than group annotation information. Honestly, like your article mentioned that sometimes the skewed annotation may get flawed result. But may it still helpful to add the annotation information as prior?
Practically, instead of hard annotation, LLM can be used to convert annotation to embeddings and then contributes to mapping matrix with prior-weights.
Do you think it's worth to be included into the packages?