6 citations · 8 across the 4 of their papers we have counts for
3 papers
cs.CL2026
Enhancing Numerical Prediction in LLMs via Smooth MMD Alignment
Zhuo Zuo, Li Yue, Wenhao Zheng +2
Despite their strong general capabilities, large language models (LLMs) often remain unreliable when outputs must be numerically precise. A key reason is the training objective: st…
cs.LG2026
Taming the Long Tail: Rebalancing Adversarial Training via Adaptive Perturbation
Lilin Zhang, Yimo Guo, Yue Li +2
Deep neural networks are highly vulnerable to adversarial examples, i.e.,small perturbations that can significantly degrade model performance. While adversarial training has become…
q-bio.BM2024
DrugLLM: Open Large Language Model for Few-shot Molecule Generation
Xianggen Liu, Yan Guo, Haoran Li +4
Large Language Models (LLMs) have made great strides in areas such as language processing and computer vision. Despite the emergence of diverse techniques to improve few-shot learn…