1 citations · 1 across the 11 of their papers we have counts for
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Proximal Supervised Fine-Tuning
Wenhong Zhu, Ruobing Xie, Rui Wang +3
Supervised fine-tuning (SFT) of foundation models often leads to poor generalization, where prior capabilities deteriorate after tuning on new tasks or domains. Inspired by trust-r…
Flexible Realignment of Language Models
Wenhong Zhu, Ruobing Xie, Weinan Zhang +1
Realignment becomes necessary when a language model (LM) fails to meet expected performance. We propose a flexible realignment framework that supports quantitative control of align…
Adding Alignment Control to Language Models
Wenhong Zhu, Weinan Zhang, Rui Wang
Post-training alignment has increasingly become a crucial factor in enhancing the usability of language models (LMs). However, the strength of alignment varies depending on individ…
Do Large Language Models Truly Understand Geometric Structures?
Xiaofeng Wang, Yiming Wang, Wenhong Zhu +1
Geometric ability is a significant challenge for large language models (LLMs) due to the need for advanced spatial comprehension and abstract thinking. Existing datasets primarily…