1 citations · 2 across the 6 of their papers we have counts for
7 papers
DS-ProGen: A Dual-Structure Deep Language Model for Functional Protein Design
Yanting Li, Jiyue Jiang, Zikang Wang +11
Inverse Protein Folding (IPF) is a critical subtask in the field of protein design, aiming to engineer amino acid sequences capable of folding correctly into a specified three-dime…
Biological Sequence with Language Model Prompting: A Survey
Jiyue Jiang, Zikang Wang, Yuheng Shan +5
Large Language models (LLMs) have emerged as powerful tools for addressing challenges across diverse domains. Notably, recent studies have demonstrated that large language models s…
Benchmarking Large Language Models on Multiple Tasks in Bioinformatics NLP with Prompting
Jiyue Jiang, Pengan Chen, Jiuming Wang +13
Large language models (LLMs) have become important tools in solving biological problems, offering improvements in accuracy and adaptability over conventional methods. Several bench…
Developing and Utilizing a Large-Scale Cantonese Dataset for Multi-Tasking in Large Language Models
Jiyue Jiang, Alfred Kar Yin Truong, Yanyu Chen +7
High-quality data resources play a crucial role in learning large language models (LLMs), particularly for low-resource languages like Cantonese. Despite having more than 85 millio…
Large Language Models in Bioinformatics: A Survey
Zhenyu Wang, Zikang Wang, Jiyue Jiang +3
Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. This survey provides a systematic review of…
How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Models
Jiyue Jiang, Pengan Chen, Liheng Chen +5
The rapid evolution of large language models (LLMs) has transformed the competitive landscape in natural language processing (NLP), particularly for English and other data-rich lan…
Data Augmentation of Multi-turn Psychological Dialogue via Knowledge-driven Progressive Thought Prompting
Jiyue Jiang, Liheng Chen, Sheng Wang +3
Existing dialogue data augmentation (DA) techniques predominantly focus on augmenting utterance-level dialogues, which makes it difficult to take dialogue contextual information in…