23 citations · 31 across the 8 of their papers we have counts for
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cs.CL2024
PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment
Zekun Moore Wang, Shawn Wang, Kang Zhu +5
Alignment of large language models (LLMs) involves training models on preference-contrastive output pairs to adjust their responses according to human preferences. To obtain such c…
cs.CL2024
PositionID: LLMs can Control Lengths, Copy and Paste with Explicit Positional Awareness
Zekun Wang, Feiyu Duan, Yibo Zhang +4
Large Language Models (LLMs) demonstrate impressive capabilities across various domains, including role-playing, creative writing, mathematical reasoning, and coding. Despite these…
cs.CL2024
MIO: A Foundation Model on Multimodal Tokens
Zekun Wang, King Zhu, Chunpu Xu +14
In this paper, we introduce MIO, a novel foundation model built on multimodal tokens, capable of understanding and generating speech, text, images, and videos in an end-to-end, aut…