8 citations · 34 across the 32 of their papers we have counts for
10 papers · 1 filter
Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?
Zihao Li, Lecheng Zheng, Bowen Jin +5
While great success has been achieved in building vision models with Contrastive Language-Image Pre-training (CLIP) over internet-scale image-text pairs, building transferable Grap…
InstructG2I: Synthesizing Images from Multimodal Attributed Graphs
Bowen Jin, Ziqi Pang, Bingjun Guo +3
In this paper, we approach an overlooked yet critical task Graph2Image: generating images from multimodal attributed graphs (MMAGs). This task poses significant challenges due to t…
Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG
Bowen Jin, Jinsung Yoon, Jiawei Han +1
Retrieval-augmented generation (RAG) empowers large language models (LLMs) to utilize external knowledge sources. The increasing capacity of LLMs to process longer input sequences…
CoDiCast: Conditional Diffusion Model for Global Weather Prediction with Uncertainty Quantification
Jimeng Shi, Bowen Jin, Jiawei Han +2
Accurate weather forecasting is critical for science and society. Yet, existing methods have not managed to simultaneously have the properties of high accuracy, low uncertainty, an…
Investigating Instruction Tuning Large Language Models on Graphs
Kerui Zhu, Bo-Wei Huang, Bowen Jin +5
Inspired by the recent advancements of Large Language Models (LLMs) in NLP tasks, there's growing interest in applying LLMs to graph-related tasks. This study delves into the capab…
A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery
Yu Zhang, Xiusi Chen, Bowen Jin +4
In many scientific fields, large language models (LLMs) have revolutionized the way text and other modalities of data (e.g., molecules and proteins) are handled, achieving superior…
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning
Qi Zhu, Da Zheng, Xiang Song +4
Text-rich graphs, which exhibit rich textual information on nodes and edges, are prevalent across a wide range of real-world business applications. Large Language Models (LLMs) hav…
Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
Bowen Jin, Chulin Xie, Jiawei Zhang +8
Large language models (LLMs), while exhibiting exceptional performance, suffer from hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment LLMs…
Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy
SeongKu Kang, Shivam Agarwal, Bowen Jin +3
Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs). However, their effectiveness is often limited in theme-specific ap…
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction
Sizhe Zhou, Yu Meng, Bowen Jin +1
Relation extraction (RE) aims to identify semantic relationships between entities within text. Despite considerable advancements, existing models predominantly require extensive an…