4 papers
WeGR: Towards Unified Generative Retrieval via Behavior-aware Tokenization and Preference Alignment
Jiajun Han, Guoxin Ma, Felix +9
Generative retrieval (GR) offers a promising paradigm for industrial search by unifying candidate generation within an autoregressive framework. However, effectively leveraging use…
Preserving Domain Generalization in Fine-Tuning via Joint Parameter Selection
Bin Pan, Shiyu Shen, Zongbin Wang +2
Domain generalization seeks to develop models trained on a limited set of source domains that are capable of generalizing effectively to unseen target domains. While the predominan…
Domain Generalization Guided by Large-Scale Pre-Trained Priors
Zongbin Wang, Bin Pan, Shiyu Shen +2
Domain generalization (DG) aims to train a model from limited source domains, allowing it to generalize to unknown target domains. Typically, DG models only employ large-scale pre-…
Domain Agnostic Conditional Invariant Predictions for Domain Generalization
Zongbin Wang, Bin Pan, Zhenwei Shi
Domain generalization aims to develop a model that can perform well on unseen target domains by learning from multiple source domains. However, recent-proposed domain generalizatio…