Closed Clover-Hill closed 1 month ago
Hi, thanks for your interest in our work! In Appendix E, we train various projectors from scratch. I am afraid we can't reuse projector trained with embedding model A to adapt to embedding model B, simply because they are not in the same representation space.
Thanks for your response.
I understand it would be inconvenient for you to open source the complete data and pipeline for paraphrase pretraining and context-aware instruction tuning since follow-up works are being conducted.
I wonder if you could share some pretrained weight of projectors trained using small embedding models. I've sent an e-mail to chengxin1998@stu.pku.edu.cn regarding this matter. Please respond to my mail if you wish to discuss privately.
Hi there, this is a very innovative paper and I really appreciate your work here.
In Appendix E, you compared the performance of different embedding models. Did you use the same projector or have you repeated the pretraining + fine-tuning process for each of these embedding models ?
I assume the output embedding spaces of different embedding models are very different and can't use the same projector. Have you tested the performance of a projector trained with retriever A but evaluated with retriever B ?
I guess my goal here is to use your pretrained projector but using a much smaller embedding model for my own project. Can you shed some light on how to achieve this ?
Thanks in advance.