2 citations · 2 across the 5 of their papers we have counts for
6 papers
BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language
Qizhi Pei, Zhimeng Zhou, Yi Duan +9
We present BioMatrix, the first multimodal foundation model that natively integrates sequences, structures, and natural language for both molecules and proteins within a single dec…
Your UnEmbedding Matrix is Secretly a Feature Lens for Text Embeddings
Songhao Wu, Zhongxin Chen, Yuxuan Liu +3
Large language models exhibit impressive zero-shot capabilities across a wide range of downstream tasks. However, they struggle to function as off-the-shelf embedding models, leadi…
HierBias: Context-Conditioned Hierarchical Media Bias Detection with Multi-Task Type Classification
Kaining Li, Ruichen Yan, Yuxin Dong
Media bias detection is a critical task for ensuring fair and balanced information dissemination, yet existing sentence-level approaches classify each sentence independently, ignor…
Towards Effective and Efficient Continual Pre-training of Large Language Models
Jie Chen, Zhipeng Chen, Jiapeng Wang +16
Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. To make the CPT approach more traceable, this paper presents…
YuLan: An Open-source Large Language Model
Yutao Zhu, Kun Zhou, Kelong Mao +35
Large language models (LLMs) have become the foundation of many applications, leveraging their extensive capabilities in processing and understanding natural language. While many o…
Mixture of In-Context Experts Enhance LLMs' Long Context Awareness
Hongzhan Lin, Ang Lv, Yuhan Chen +4
Many studies have revealed that large language models (LLMs) exhibit uneven awareness of different contextual positions. Their limited context awareness can lead to overlooking cri…