1 citations · 1 across the 3 of their papers we have counts for
4 papers
How Does Post-Training Shape LLM Value Preferences?
Yang Liu, Shaoyang Xu, Masahiro Kaneko +3
During post-training, large language models (LLMs) learn value preferences from corpora that may contain imbalanced proportions of opinions associated with different demographic gr…
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
Language Lives in Sparse Dimensions: Toward Interpretable and Efficient Multilingual Control for Large Language Models
Chengzhi Zhong, Fei Cheng, Qianying Liu +3
Large language models exhibit strong multilingual capabilities despite limited exposure to non-English data. Prior studies show that English-centric large language models map multi…
Beyond English-Centric LLMs: What Language Do Multilingual Language Models Think in?
Chengzhi Zhong, Fei Cheng, Qianying Liu +5
In this study, we investigate whether non-English-centric LLMs, despite their strong performance, `think' in their respective dominant language: more precisely, `think' refers to h…