REZE: Representation Regularization for Domain-adaptive Text Embedding Pre-finetuning
Recent text embedding models are often adapted to specialized domains via contrastive pre-finetuning (PFT) on a naive collection of scattered, heterogeneous tasks. However, this approach often introduces task-induced bias alongside domain knowledge, leading to uncontrolled representation shifts that distort the pretrained embedding geometry and cause substantial performance degradation. To address this issue, we propose REZE, a representation regularization framework that explicitly controls representation shift during embedding pre-finetuning. REZE operates on the relations of anchor-positive pairs and decomposes them in an eigenspace. It then measures task-wise dispersion along each eig...
| Authors | Seungmin Lee, Jeonghwan Lee, Hyunkuk Lim et al. |
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| Primary category | cs.CL |
| Published | 19 April 2026 |
| Last updated at source | 21 April 2026 |
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