1 citations · 1 across the 6 of their papers we have counts for
4 papers · 1 filter
A Unifying Lens on Supervised Fine-Tuning Through Target Distribution Design
Tong Xie, Yuanhao Ban, Yunqi Hong +3
Supervised fine-tuning (SFT) typically maximizes the likelihood of every token in a demonstrated trajectory. However, an observed token can be non-unique, noisy, or misaligned with…
ELSA: Efficient LLM-Centric Split Aggregation for Privacy-Aware Hierarchical Federated Learning over the Network Edge
Xiaohong Yang, Tong Xie, Minghui Liwang +6
Training large language models (LLMs) in resource-constrained edge networks poses fundamental challenges in computation, communication, data heterogeneity, and privacy. To address…
When Distance Distracts: Representation Distance Bias in BT-Loss for Reward Models
Tong Xie, Andrew Bai, Yuanhao Ban +3
Reward models are central to Large Language Model (LLM) alignment within the framework of RLHF. The standard objective used in reward modeling is the Bradley-Terry (BT) loss, which…
Data Attribution for Diffusion Models: Timestep-induced Bias in Influence Estimation
Tong Xie, Haoyu Li, Andrew Bai +1
Data attribution methods trace model behavior back to its training dataset, offering an effective approach to better understand ''black-box'' neural networks. While prior research…