6 citations · 6 across the 11 of their papers we have counts for
7 papers · 1 filter
DVAGen: Dynamic Vocabulary Augmented Generation
Wei Du, Nuowei Liu, Jie Wang +4
Language models trained with a fixed vocabulary struggle to generalize to novel or out-of-vocabulary words, limiting their flexibility in handling diverse token combinations. Exist…
TASO: Task-Aligned Sparse Optimization for Parameter-Efficient Model Adaptation
Daiye Miao, Yufang Liu, Jie Wang +6
LoRA has become one of the most widely used parameter-efficient fine-tuning methods due to its simplicity and effectiveness. However, numerous studies have shown that LoRA often in…
On Support Samples of Next Word Prediction
Yuqian Li, Yupei Du, Yufang Liu +3
Language models excel in various tasks by making complex decisions, yet understanding the rationale behind these decisions remains a challenge. This paper investigates \emph{data-c…
PDFBench: A Benchmark for De novo Protein Design from Function
Jiahao Kuang, Nuowei Liu, Jie Wang +3
Function-guided protein design is a crucial task with significant applications in drug discovery and enzyme engineering. However, the field lacks a unified and comprehensive evalua…
Protein Design with Dynamic Protein Vocabulary
Nuowei Liu, Jiahao Kuang, Yanting Liu +4
Protein design is a fundamental challenge in biotechnology, aiming to design novel sequences with specific functions within the vast space of possible proteins. Recent advances in…
The Role of Visual Modality in Multimodal Mathematical Reasoning: Challenges and Insights
Yufang Liu, Yao Du, Tao Ji +6
Recent research has increasingly focused on multimodal mathematical reasoning, particularly emphasizing the creation of relevant datasets and benchmarks. Despite this, the role of…
Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs
Tao Ji, Bin Guo, Yuanbin Wu +6
Multi-head Latent Attention (MLA) is an innovative architecture proposed by DeepSeek, designed to ensure efficient and economical inference by significantly compressing the Key-Val…