3 papers
cs.LG2025
Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA
Shuangyi Chen, Yuanxin Guo, Yue Ju +3
Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) optimize federated training by reducing computational and communication costs. We propose RoLoRA, a f…
cs.CL2024
SAFETY-J: Evaluating Safety with Critique
Yixiu Liu, Yuxiang Zheng, Shijie Xia +4
The deployment of Large Language Models (LLMs) in content generation raises significant safety concerns, particularly regarding the transparency and interpretability of content eva…
cs.CL2024
BeHonest: Benchmarking Honesty in Large Language Models
Steffi Chern, Zhulin Hu, Yuqing Yang +5
Previous works on Large Language Models (LLMs) have mainly focused on evaluating their helpfulness or harmlessness. However, honesty, another crucial alignment criterion, has recei…