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Bridging Code Graphs and Large Language Models for Better Code Understanding
Zeqi Chen, Zhaoyang Chu, Yi Gui +3
Large Language Models (LLMs) have demonstrated remarkable performance in code intelligence tasks such as code generation, summarization, and translation. However, their reliance on…
Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning
Zihao Zhao, Xinlong Zhai, Jinyu Yang +1
Foundation models have achieved great success in natural language processing (NLP) and computer vision (CV). Their success largely stems from the ability to integrate multi-domain…
HeTGB: A Comprehensive Benchmark for Heterophilic Text-Attributed Graphs
Shujie Li, Yuxia Wu, Yuan Fang +1
Graph neural networks (GNNs) have demonstrated success in modeling relational data primarily under the assumption of homophily. However, many real-world graphs exhibit heterophily,…
Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases
Michael Y. Hu, Jackson Petty, Chuan Shi +2
Pretraining language models on formal language can improve their acquisition of natural language. Which features of the formal language impart an inductive bias that leads to effec…