New research explores efficient LLM alignment and federated fine-tuning

COVERAGE [8]

1. arXiv cs.AI TIER_1 English(EN) · Qitao Tan, Xiaoying Song, Arman Akbari, Arash Akbari, Yanzhi Wang, Xiaoming Zhai, Lingzi Hong, Zhen Xiang, Jin Lu, Geng Yuan · 2026-05-26 04:00

   Palette: A Modular, Controllable, and Efficient Framework for On-demand Authorized Safety Alignment Relaxation in LLMs

   arXiv:2605.24154v1 Announce Type: new Abstract: Current safety alignment of foundation models largely follows a \emph{one-size-fits-all} paradigm, applying the same refusal policy across users and contexts. As a result, models may refuse requests that are unsafe for general users…

2. arXiv cs.AI TIER_1 English(EN) · Dylan Feng, Pragya Srivastava, Anca Dragan, Cassidy Laidlaw · 2026-05-26 04:00

   Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs

   arXiv:2605.21602v2 Announce Type: replace Abstract: Many safety and alignment failures of large language models (LLMs) occur due to out-of-distribution (OOD) situations: unusual prompt or response patterns that are unforeseen by model developers. We systematically study whether L…

3. arXiv cs.LG TIER_1 English(EN) · Yonghui Yang, Wenjian Tao, Jilong Liu, Xingyu Zhu, Junfeng Fang, Weibiao Huang, Le Wu, Richang Hong, Tat-Sent Chua · 2026-05-22 04:00

   Revisiting Robustness for LLM Safety Alignment via Selective Geometry Control

   arXiv:2602.07340v2 Announce Type: replace Abstract: Safety alignment of large language models remains brittle under domain shift and noisy preference supervision. Most existing robust alignment methods focus on uncertainty in alignment data, while overlooking optimization-induced…

4. arXiv cs.CL TIER_1 English(EN) · Hao Chen, Qi Zhang, Liyao Li, Zhanming Shen, Wentao Ye, Lirong Gao, Ningtao Wang, Xing Fu, Xiaoyu Shen, Junbo Zhao · 2026-05-22 04:00

   From Parameters to Data: A Task-Parameter-Guided Fine-Tuning Pipeline for Efficient LLM Alignment

   arXiv:2605.21558v1 Announce Type: cross Abstract: Adapting Large Language Models (LLMs) to specialized domains typically incurs high data and computational overhead. While prior efficiency efforts have largely treated data selection and parameter-efficient fine-tuning as isolated…

5. Hugging Face Daily Papers TIER_1 English(EN) · 2026-05-20 18:08

   Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs

   Many safety and alignment failures of large language models (LLMs) occur due to out-of-distribution (OOD) situations: unusual prompt or response patterns that are unforeseen by model developers. We systematically study whether LLM monitoring pipelines can detect these OOD alignme…

6. Hugging Face Daily Papers TIER_1 English(EN) · 2026-05-12 09:41

   Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs

   Federated fine-tuning of large language models is commonly formulated as a parameter aggregation problem. However, even parameter-efficient methods require transmitting large collections of trainable weights, assume aligned architectures, and rely on white-box access to model par…

7. arXiv stat.ML TIER_1 English(EN) · Shuaida He, Liwen Chen, Long Feng · 2026-05-21 04:00

   Federated LoRA Fine-Tuning for LLMs via Collaborative Alignment

   arXiv:2605.21217v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) has emerged as a powerful tool for parameter-efficient fine-tuning of large language models (LLMs). This paper studies LoRA under a federated learning setting, enabling collaborative fine-tuning across cli…

8. arXiv stat.ML TIER_1 English(EN) · Long Feng · 2026-05-20 14:12

   Federated LoRA Fine-Tuning for LLMs via Collaborative Alignment

   Low-rank adaptation (LoRA) has emerged as a powerful tool for parameter-efficient fine-tuning of large language models (LLMs). This paper studies LoRA under a federated learning setting, enabling collaborative fine-tuning across clients while preserving parameter efficiency. We f…