Search Results for author:
Found 24 papers, 18 papers with code
Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?
In this work, to address these issues, we leverage multi-modal prompt learning to effectively adapt pre-trained GNN to downstream tasks and data, given only a few semantically labeled samples, each with extremely weak text supervision.
InstructG2I: Synthesizing Images from Multimodal Attributed Graphs
In this paper, we approach an overlooked yet critical task Graph2Image: generating images from multimodal attributed graphs (MMAGs).
Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG
Retrieval-augmented generation (RAG) empowers large language models (LLMs) to utilize external knowledge sources.
CoDiCast: Conditional Diffusion Model for Weather Prediction with Uncertainty Quantification
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.
Investigating Instruction Tuning Large Language Models on Graphs
Inspired by the recent advancements of Large Language Models (LLMs) in NLP tasks, there's growing interest in applying LLMs to graph-related tasks.
A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery
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.
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning
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.
Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
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.
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
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.
Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy
Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs).
RAM-EHR: Retrieval Augmentation Meets Clinical Predictions on Electronic Health Records
We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on Electronic Health Records (EHRs).
Grasping the Essentials: Tailoring Large Language Models for Zero-Shot Relation Extraction
(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.
Large Language Models on Graphs: A Comprehensive Survey
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).
Scalable and Effective Generative Information Retrieval
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.
Chain-of-Factors Paper-Reviewer Matching
To address this issue, we propose a unified model for paper-reviewer matching that jointly considers semantic, topic, and citation factors.
Language Models As Semantic Indexers
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.
Learning Multiplex Representations on Text-Attributed Graphs with One Language Model Encoder
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.
Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers
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).
Text-Augmented Open Knowledge Graph Completion via Pre-Trained Language Models
The mission of open knowledge graph (KG) completion is to draw new findings from known facts.
Patton: Language Model Pretraining on Text-Rich Networks
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).
Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks
Edges in many real-world social/information networks are associated with rich text information (e. g., user-user communications or user-product reviews).
The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study
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.
Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks
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.
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
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.
