9 papers
ERNIE 5.0 Technical Report
Haifeng Wang, Hua Wu, Tian Wu +432
In this report, we introduce ERNIE 5.0, a natively autoregressive foundation model desinged for unified multimodal understanding and generation across text, image, video, and audio…
Understanding Subword Compositionality of Large Language Models
Qiwei Peng, Yekun Chai, Anders Søgaard
Large language models (LLMs) take sequences of subwords as input, requiring them to effective compose subword representations into meaningful word-level representations. In this pa…
Debiasing Multilingual LLMs in Cross-lingual Latent Space
Qiwei Peng, Guimin Hu, Yekun Chai +1
Debiasing techniques such as SentDebias aim to reduce bias in large language models (LLMs). Previous studies have evaluated their cross-lingual transferability by directly applying…
Curiosity-Driven Reinforcement Learning from Human Feedback
Haoran Sun, Yekun Chai, Shuohuan Wang +3
Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences, but often at the cost of reduced output dive…
MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions
Yekun Chai, Haoran Sun, Huang Fang +3
Reinforcement learning from human feedback (RLHF) has demonstrated effectiveness in aligning large language models (LLMs) with human preferences. However, token-level RLHF suffers…
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code
Taishi Nakamura, Mayank Mishra, Simone Tedeschi +42
Pretrained language models are an integral part of AI applications, but their high computational cost for training limits accessibility. Initiatives such as Bloom and StarCoder aim…