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Guangyue Lu

2 accepted papers

2025

Graph-R1: Incentivizing the Zero-Shot Graph Learning Capability in LLMs via Explicit Reasoning

EMNLP 2025

Generalizing to unseen graph tasks without task-specific supervision remains challenging. Graph Neural Networks (GNNs) are limited by fixed label spaces, while Large Language Models (LLMs) lack structural inductive biases. Recent advances in Large Reasoning Models (LRMs) provide a zero-shot alternat

2025

UniGTE: Unified Graph–Text Encoding for Zero-Shot Generalization across Graph Tasks and Domains

NeurIPS 2025poster

Generalizing to unseen graph tasks without task-specific supervision is challenging: conventional graph neural networks are typically tied to a fixed label space, while large language models (LLMs) struggle to capture graph structure. We introduce UniGTE, an instruction-tuned encoder–decoder framewo…

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