5 papers · 1 filter
Bayesian Sparse Low-Rank Adaptation for Large Language Model Uncertainty Estimation
Jijie Zhang, Zhe Ren, Quan Zhang +1
Large language models (LLMs) exhibit remarkable reasoning capabilities, but their task-specific fine-tuning is notoriously plagued by overconfidence, severely hindering trustworthy…
SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity
Liyang Yuan, Yibo Yang, Dandan Guo +2
Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift that severely hampers global…
FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering
Liyang Yuan, Yibo Yang, Dandan Guo
Federated Learning (FL) enables decentralized training without data sharing, but suffers from statistical heterogeneity across clients, leading to client drift, poor generalization…
GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents
Zhe Ren, Yibo Yang, Yimeng Chen +7
Memory benchmarks for LLM agents largely assume single-user settings, leaving shared assistants for hospitals, workplaces, campuses, and households understudied. In these deploymen…
Safeguarding LLM Fine-tuning via Push-Pull Distributional Alignment
Haozhong Wang, Zhuo Li, Yibo Yang +3
The inherent safety alignment of Large Language Models (LLMs) is prone to erosion during fine-tuning, even when using seemingly innocuous datasets. While existing defenses attempt…