40 citations · 112 across the 11 of their papers we have counts for
11 papers
A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following
Yin Fang, Xinle Deng, Kangwei Liu +5
Large language models excel at interpreting complex natural language instructions, enabling them to perform a wide range of tasks. In the life sciences, single-cell RNA sequencing…
DRAK: Unlocking Molecular Insights with Domain-Specific Retrieval-Augmented Knowledge in LLMs
Jinzhe Liu, Xiangsheng Huang, Zhuo Chen +1
Large Language Models (LLMs) encounter challenges with the unique syntax of specific domains, such as biomolecules. Existing fine-tuning or modality alignment techniques struggle t…
Noise-powered Multi-modal Knowledge Graph Representation Framework
Zhuo Chen, Yin Fang, Yichi Zhang +5
The rise of Multi-modal Pre-training highlights the necessity for a unified Multi-Modal Knowledge Graph (MMKG) representation learning framework. Such a framework is essential for…
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Zhuo Chen, Yichi Zhang, Yin Fang +12
Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for…
ChatCell: Facilitating Single-Cell Analysis with Natural Language
Yin Fang, Kangwei Liu, Ningyu Zhang +7
As Large Language Models (LLMs) rapidly evolve, their influence in science is becoming increasingly prominent. The emerging capabilities of LLMs in task generalization and free-for…
Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering
Yichi Zhang, Zhuo Chen, Yin Fang +4
Deploying large language models (LLMs) to real scenarios for domain-specific question answering (QA) is a key thrust for LLM applications, which poses numerous challenges, especial…