1 code implementation • 9 Oct 2024 • Bowen Jin, Ziqi Pang, Bingjun Guo, Yu-Xiong Wang, Jiaxuan You, Jiawei Han
In this paper, we approach an overlooked yet critical task Graph2Image: generating images from multimodal attributed graphs (MMAGs).
no code implementations • 8 Oct 2024 • Bowen Jin, Jinsung Yoon, Jiawei Han, Sercan O. Arik
Retrieval-augmented generation (RAG) empowers large language models (LLMs) to utilize external knowledge sources.
1 code implementation • 9 Sep 2024 • Jimeng Shi, Bowen Jin, Jiawei Han, Giri Narasimhan
Our conditional diffusion model, CoDiCast, can generate 3-day global weather forecasts, at 6-hour steps and $5. 625^\circ$ latitude-longitude resolution, for over 5 variables, in about 12 minutes on a commodity A100 GPU machine with 80GB memory.
1 code implementation • 10 Aug 2024 • Kerui Zhu, Bo-Wei Huang, Bowen Jin, Yizhu Jiao, Ming Zhong, Kevin Chang, Shou-De Lin, Jiawei Han
Inspired by the recent advancements of Large Language Models (LLMs) in NLP tasks, there's growing interest in applying LLMs to graph-related tasks.
1 code implementation • 16 Jun 2024 • Yu Zhang, Xiusi Chen, Bowen Jin, Sheng Wang, Shuiwang Ji, Wei Wang, Jiawei Han
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 performance in various applications and augmenting the scientific discovery process.
no code implementations • 28 Apr 2024 • Qi Zhu, Da Zheng, Xiang Song, Shichang Zhang, Bowen Jin, Yizhou Sun, George Karypis
Inspired by this, we introduce Graph-aware Parameter-Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning with LLMs on text-rich graphs.
1 code implementation • 10 Apr 2024 • Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy, Yu Zhang, Zheng Li, Ruirui Li, Xianfeng Tang, Suhang Wang, Yu Meng, Jiawei Han
Then, we propose a simple and effective framework called Graph Chain-of-thought (Graph-CoT) to augment LLMs with graphs by encouraging LLMs to reason on the graph iteratively.
1 code implementation • 15 Mar 2024 • Tianxin Wei, Bowen Jin, Ruirui Li, Hansi Zeng, Zhengyang Wang, Jianhui Sun, Qingyu Yin, Hanqing Lu, Suhang Wang, Jingrui He, Xianfeng Tang
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration.
1 code implementation • 7 Mar 2024 • SeongKu Kang, Shivam Agarwal, Bowen Jin, Dongha Lee, Hwanjo Yu, Jiawei Han
Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs).
1 code implementation • 25 Feb 2024 • ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Bowen Jin, May D. Wang, Joyce C. Ho, Carl Yang
We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs).
1 code implementation • 17 Feb 2024 • Sizhe Zhou, Yu Meng, Bowen Jin, Jiawei Han
(3) We expand pattern coverage and mitigate bias from initial seeds by integrating feedback from the SLM's predictions on the unlabeled corpus and the synthesis history.
1 code implementation • 5 Dec 2023 • Bowen Jin, Gang Liu, Chi Han, Meng Jiang, Heng Ji, Jiawei Han
Besides, although LLMs have shown their pure text-based reasoning ability, it is underexplored whether such ability can be generalized to graphs (i. e., graph-based reasoning).
2 code implementations • 15 Nov 2023 • Hansi Zeng, Chen Luo, Bowen Jin, Sheikh Muhammad Sarwar, Tianxin Wei, Hamed Zamani
This paper represents an important milestone in generative retrieval research by showing, for the first time, that generative retrieval models can be trained to perform effectively on large-scale standard retrieval benchmarks.
no code implementations • 23 Oct 2023 • Yu Zhang, Yanzhen Shen, SeongKu Kang, Xiusi Chen, Bowen Jin, Jiawei Han
To address this issue, we propose a unified model for paper-reviewer matching that jointly considers semantic, topic, and citation factors.
1 code implementation • 11 Oct 2023 • Bowen Jin, Hansi Zeng, Guoyin Wang, Xiusi Chen, Tianxin Wei, Ruirui Li, Zhengyang Wang, Zheng Li, Yang Li, Hanqing Lu, Suhang Wang, Jiawei Han, Xianfeng Tang
Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs.
1 code implementation • 10 Oct 2023 • Bowen Jin, Wentao Zhang, Yu Zhang, Yu Meng, Han Zhao, Jiawei Han
Mainstream text representation learning methods use pretrained language models (PLMs) to generate one embedding for each text unit, expecting that all types of relations between texts can be captured by these single-view embeddings.
1 code implementation • 24 Jun 2023 • Yu Zhang, Bowen Jin, Xiusi Chen, Yanzhen Shen, Yunyi Zhang, Yu Meng, Jiawei Han
Instead of relying on human-annotated training samples to build a classifier, weakly supervised scientific paper classification aims to classify papers only using category descriptions (e. g., category names, category-indicative keywords).
1 code implementation • 24 May 2023 • Pengcheng Jiang, Shivam Agarwal, Bowen Jin, Xuan Wang, Jimeng Sun, Jiawei Han
The mission of open knowledge graph (KG) completion is to draw new findings from known facts.
no code implementations • 20 May 2023 • Bowen Jin, Wentao Zhang, Yu Zhang, Yu Meng, Xinyang Zhang, Qi Zhu, Jiawei Han
A real-world text corpus sometimes comprises not only text documents but also semantic links between them (e. g., academic papers in a bibliographic network are linked by citations and co-authorships).
1 code implementation • 21 Feb 2023 • Bowen Jin, Yu Zhang, Yu Meng, Jiawei Han
Edges in many real-world social/information networks are associated with rich text information (e. g., user-user communications or user-product reviews).
1 code implementation • 7 Feb 2023 • Yu Zhang, Bowen Jin, Qi Zhu, Yu Meng, Jiawei Han
Due to the exponential growth of scientific publications on the Web, there is a pressing need to tag each paper with fine-grained topics so that researchers can track their interested fields of study rather than drowning in the whole literature.
1 code implementation • 20 May 2022 • Bowen Jin, Yu Zhang, Qi Zhu, Jiawei Han
In heterogeneous text-rich networks, this task is more challenging due to (1) presence or absence of text: Some nodes are associated with rich textual information, while others are not; (2) diversity of types: Nodes and edges of multiple types form a heterogeneous network structure.
no code implementations • 22 Apr 2021 • Junhan Yang, Zheng Liu, Bowen Jin, Jianxun Lian, Defu Lian, Akshay Soni, Eun Yong Kang, Yajun Wang, Guangzhong Sun, Xing Xie
For the sake of efficient recommendation, conventional methods would generate user and advertisement embeddings independently with a siamese transformer encoder, such that approximate nearest neighbour search (ANN) can be leveraged.