2 papers
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…