5 papers · 1 filter
Struc-EMB: The Potential of Structure-Aware Encoding in Language Embeddings
Shikun Liu, Haoyu Wang, Mufei Li +1
Text embeddings from Large Language Models (LLMs) have become foundational for numerous applications. However, these models typically operate on raw text, overlooking the rich stru…
RoFt-Mol: Benchmarking Robust Fine-Tuning with Molecular Graph Foundation Models
Shikun Liu, Deyu Zou, Nima Shoghi +3
In the era of foundation models, fine-tuning pre-trained models for specific downstream tasks has become crucial. This drives the need for robust fine-tuning methods to address cha…
Graph-KV: Breaking Sequence via Injecting Structural Biases into Large Language Models
Haoyu Wang, Peihao Wang, Mufei Li +4
Modern large language models (LLMs) are inherently auto-regressive, requiring input to be serialized into flat sequences regardless of their structural dependencies. This serializa…
Structural Alignment Improves Graph Test-Time Adaptation
Hans Hao-Hsun Hsu, Shikun Liu, Han Zhao +1
Graph-based learning excels at capturing interaction patterns in diverse domains like recommendation, fraud detection, and particle physics. However, its performance often degrades…
Model Generalization on Text Attribute Graphs: Principles with Large Language Models
Haoyu Wang, Shikun Liu, Rongzhe Wei +1
Large language models (LLMs) have recently been introduced to graph learning, aiming to extend their zero-shot generalization success to tasks where labeled graph data is scarce. A…