Papers by Chuang Zhou
Each graph is a new language: Graph Learning with LLMs (2025.findings-acl)
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| Challenge: | Natural language is used to describe graphs, but graph descriptions become verbose and only relying on attribute embeddings limits LLM’s ability to capture adequate graph structural information. |
| Approach: | They propose a graph-defined language for large language model that translates the graph into a corpus instead of graph descriptions and pre-trains LLMs on this corpus to adequately understand the graph. |
| Outcome: | Experiments on five datasets show that the proposed framework outperforms description-based and embedding-based baselines by efficiently modeling different orders of neighbors. |
Text-Attributed Graph Learning with Coupled Augmentations (2025.coling-main)
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| Challenge: | Existing models focus on either the text attribute or the graph structure, neglecting the other aspect. |
| Approach: | They propose a model that combines the strengths of both text-learning and graph-learning models in parallel. |
| Outcome: | The proposed model outperforms existing models on diverse datasets. |
Collision to Cognition: Hash-Driven Graph Construction for Efficient RAG (2026.acl-long)
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Chuang Zhou, Zheng Yuan, Linhao Luo, Zhaozhuo Xu, Yilin Xiao, Junnan Dong, Siyu An, di Yin, Xing Sun, Xiao Huang
| Challenge: | Retrieval-augmented generation (RAG) has been used for enhancing large language models with external knowledge. |
| Approach: | They propose a framework for mining efficient graph structures via hashing to enhance RAG . they adopt an inductive paradigm where global graph structure emerges from local hash collisions . |
| Outcome: | The proposed framework outperforms existing baselines while requiring no GPU resources or token budget. |
Taming Language Models for Text-attributed Graph Learning with Decoupled Aggregation (2025.acl-long)
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| Challenge: | Existing approaches to learning text-attributed graphs neglect interaction between textual and structural information. |
| Approach: | They propose a framework that integrates textual and structural information into TAG learning . they propose combining semantic aggregation and structural aggregations to improve learning a . |
| Outcome: | The proposed framework outperforms state-of-the-art learning methods while requiring less resources. |
LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation (2026.acl-long)
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Yilin Xiao, Jin Chen, Qinggang Zhang, Yujing Zhang, Chuang Zhou, Longhao Yang, Lingfei Ren, Xin Yang, Xiao Huang
| Challenge: | Graph-based Retrieval-Augmented Generation (GraphRAG) enhances the reasoning capabilities of Large Language Models (LLMs) however, traditional RAG attacks are difficult to pose an effective threat to GraphRAg systems. |
| Approach: | They propose a novel attack framework that targets logical reasoning rather than injecting false contents into GraphRAG systems by grounding their responses in structured knowledge graphs. |
| Outcome: | The proposed framework outperforms state-of-the-art attacks on GraphRAG systems in both effectiveness and stealth. |
Query-Aware Knowledge Retrieval via Hyperbolic Structuring (2026.acl-long)
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Chuang Zhou, Junnan Dong, Yilin Xiao, Shengyuan Chen, Su Dong, di Yin, Xing Sun, Zhaozhuo Xu, Xiao Huang
| Challenge: | Existing approaches focus primarily on retrieving isolated factual knowledge entities while neglecting the critical reasoning relationships. |
| Approach: | They propose a query-centric retrieval framework that explicitly integrates structured knowledge graphs to support complex reasoning tasks. |
| Outcome: | Extensive experiments on three benchmark datasets show that HyperRAG outperforms baselines. |
QUEST: Efficient Extreme Multi-Label Text Classification with Large Language Models on Commodity Hardware (2024.findings-emnlp)
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| Challenge: | Extreme multi-label text classification (EMTC) involves predicting multiple labels from a vast pool of candidates based on a user’s textual query. |
| Approach: | They propose a Quantized and Efficient Learning with Sampling Technique that uses a hash sampling module to reduce the data volume to one-fourth of its original size. |
| Outcome: | Extensive experiments show that QUEST outperforms existing methods while requiring fewer computational resources. |