Minh-Phuc Truong

EMO: Embedding Model Distillation via Intra-Model Relation and Optimal Transport Alignments

Knowledge distillation (KD) is crucial for compressing large text embedding models, but faces challenges when teacher and student models use different tokenizers (Cross-Tokenizer KD - CTKD). Vocabulary mismatches impede the transfer of relational knowledge encoded in deep representations, such as hi