4 citations · 9 across the 20 of their papers we have counts for
5 papers · 1 filter
SPARC: Scalable Path-Specific Counterfactual Fairness via Causal Conditional Independence
Bowei Tian, Yexiao He, Ziyao Wang +3
Deep learning models exhibit fairness concerns when predictions are inadvertently influenced by sensitive attributes. However, existing attempts to make Path-Specific Counterfactua…
Revisiting Federated Fine-Tuning: A Single Communication Round is Enough for Foundation Models
Ziyao Wang, Bowei Tian, Yexiao He +6
The recent advancement of foundation models (FMs) has increased the demand for fine-tuning these models on large-scale cross-domain datasets. To address this, federated fine-tuning…
Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild
Xinyu Zhao, Guoheng Sun, Ruisi Cai +13
As Large Language Models (LLMs) excel across tasks and specialized domains, scaling LLMs based on existing models has garnered significant attention, which faces the challenge of d…
FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations
Ziyao Wang, Zheyu Shen, Yexiao He +4
The rapid development of Large Language Models (LLMs) has been pivotal in advancing AI, with pre-trained LLMs being adaptable to diverse downstream tasks through fine-tuning. Feder…
SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning
Yexiao He, Ziyao Wang, Zheyu Shen +5
The pre-trained Large Language Models (LLMs) can be adapted for many downstream tasks and tailored to align with human preferences through fine-tuning. Recent studies have discover…