4 papers
Farewell to Item IDs: Unlocking the Scaling Potential of Large Ranking Models via Semantic Tokens
Zhen Zhao, Tong Zhang, Jie Xu +5
Recent studies on scaling up ranking models have achieved substantial improvement for recommendation systems and search engines. However, most large-scale ranking systems rely on i…
FedCARE: Federated Unlearning with Conflict-Aware Projection and Relearning-Resistant Recovery
Yue Li, Mingmin Chu, Xilei Yang +5
Federated learning (FL) enables collaborative model training without centralizing raw data, but privacy regulations such as the right to be forgotten require FL systems to remove t…
MUDDFormer: Breaking Residual Bottlenecks in Transformers via Multiway Dynamic Dense Connections
Da Xiao, Qingye Meng, Shengping Li +1
We propose MUltiway Dynamic Dense (MUDD) connections, a simple yet effective method to address the limitations of residual connections and enhance cross-layer information flow in T…
Benchmarking and Understanding Compositional Relational Reasoning of LLMs
Ruikang Ni, Da Xiao, Qingye Meng +3
Compositional relational reasoning (CRR) is a hallmark of human intelligence, but we lack a clear understanding of whether and how existing transformer large language models (LLMs)…