13 citations · 22 across the 7 of their papers we have counts for
9 papers
Joint, Decoupled, or Agent: When Each Multimodal Alignment Paradigm Wins
Yi Fang, Gaurav Srivastava, Sirui Ding +1
Jointly pretrained multimodal encoders are a common way to combine modalities, while alternatives integrate their information during downstream training or inference. When does eac…
Cache2LoRA: Understanding and Improving Context Internalization
Yi Fang, Gaurav Srivastava, Zhenyu Bi +2
Context-to-LoRA methods encode documents as low-rank parameter updates, allowing language models to answer without retaining the source text in their decoding context. How reliably…
BioArc: Discovering Optimal Neural Architectures for Biological Foundation Models
Yi Fang, Haoran Xu, Jiaxin Han +4
Foundation models have revolutionized various fields such as natural language processing (NLP) and computer vision (CV). While efforts have been made to transfer the success of the…
GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphs
Yi Fang, Bowen Jin, Jiacheng Shen +3
The rapid development of Multimodal Large Language Models (MLLMs) has enabled the integration of multiple modalities, including texts and images, within the large language model (L…
Large Language Models for Disease Diagnosis: A Scoping Review
Shuang Zhou, Zidu Xu, Mian Zhang +14
Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intellige…
Interpretable Differential Diagnosis with Dual-Inference Large Language Models
Shuang Zhou, Mingquan Lin, Sirui Ding +4
Automatic differential diagnosis (DDx) is an essential medical task that generates a list of potential diseases as differentials based on patient symptom descriptions. In practice,…
LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting
Yu-Neng Chuang, Songchen Li, Jiayi Yuan +11
Time Series Forecasting (TSF) has long been a challenge in time series analysis. Inspired by the success of Large Language Models (LLMs), researchers are now developing Large Time…
MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction
Yuyan Liu, Sirui Ding, Sheng Zhou +2
Molecular property prediction (MPP) is a fundamental and crucial task in drug discovery. However, prior methods are limited by the requirement for a large number of labeled molecul…
UniGLM: Training One Unified Language Model for Text-Attributed Graph Embedding
Yi Fang, Dongzhe Fan, Sirui Ding +2
Representation learning on text-attributed graphs (TAGs), where nodes are represented by textual descriptions, is crucial for textual and relational knowledge systems and recommend…