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cs.CL2025
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…
cs.CL2025
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…
cs.CL2025
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…