---
title: "Who cites Xiaoxin He — 179 papers of our library"
description: "179 papers of the Elamur Research library cite the works of Xiaoxin He, found in their bibliographies; the most cited first."
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---
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# Who cites Xiaoxin He

190 citations of their works from 179 papers of our library (of the 149,439 whose bibliographies we read) · 47 with a page here, the most cited first · [← Xiaoxin He](https://md.research.elamur.ai/free/en/authors/xiaoxin-he/)

1. [**Retrieval-Augmented Generation for Large Language Models: A Survey arXiv:2312.10997**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,663 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
2. [**Retrieval-Augmented Generation for AI-Generated Content: A Survey arXiv:2402.19473**Penghao Zhao, Hailin Zhang, Qinhan Yu, et al.*2024 · 301 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2024-retrieval-augmented-generation-ai-generated/)
3. **[Graph Retrieval-Augmented Generation: A Survey](https://md.research.elamur.ai/free/en/papers/peng-2024-graph-retrieval-augmented-generation-survey/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Boci Peng, Yun Zhu, Yongchao Liu, et al.*2024 · 276 citations*
4. **[GraphGPT: Graph Instruction Tuning for Large Language Models](https://md.research.elamur.ai/free/en/papers/tang-2023-graphgpt-graph-instruction-tuning-large/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Jiabin Tang, Yuhao Yang, Wei Wei, et al.*2023 · 268 citations*
5. **[G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering](https://md.research.elamur.ai/free/en/papers/he-2024-g-retriever-retrieval-augmented-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xiaoxin He, Yijun Tian, Yifei Sun, et al.*2024 · 236 citations*
6. [**Retrieval-Augmented Generation with Graphs (GraphRAG) arXiv:2501.00309**Haoyu Han, Yu Wang, Harry Shomer, et al.*2025 · 87 citations*](https://md.research.elamur.ai/free/en/papers/han-2025-retrieval-augmented-generation-graphs-graphrag/)
7. **[Knowledge Graph Retrieval-Augmented Generation for LLM-based Recommendation](https://md.research.elamur.ai/free/en/papers/wang-2025-knowledge-graph-retrieval-augmented-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shijie Wang, Wenqi Fan, Yue Feng, et al.*2025 · 77 citations*
8. [**A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models arXiv:2501.13958**Qinggang Zhang, Shengyuan Chen, Yuanchen Bei, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-graph-retrieval-augmented-generation/)
9. **[OG-RAG: Ontology-grounded retrieval-augmented generation for large language models](https://md.research.elamur.ai/free/en/papers/sharma-2025-og-rag-ontology-grounded-retrieval/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kartik Sharma, Peeyush Kumar, Yunqing Li*2025 · 37 citations*
10. [**Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs arXiv:2507.09477**Yangning Li, Weizhi Zhang, Yuyao Yang, et al.*2025 · 17 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-towards-agentic-rag-deep-reasoning/)
11. **[Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answering](https://md.research.elamur.ai/free/en/papers/sui-2025-can-knowledge-graphs-make-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuan Sui, Yufei He, Zifeng Ding, et al.*2025 · 16 citations*
12. **[Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions](https://md.research.elamur.ai/free/en/papers/yu-2026-spatial-rag-spatial-retrieval-augmented/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dazhou Yu, Riyang Bao, Ruiyu Ning, et al.*2026 · 12 citations*
13. **[Empowering GraphRAG with Knowledge Filtering and Integration](https://md.research.elamur.ai/free/en/papers/guo-2025-empowering-graphrag-knowledge-filtering-integration/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kai Guo, Harry Shomer, Shenglai Zeng, et al.*2025 · 12 citations*
14. **[MoLoRAG: Bootstrapping Document Understanding via Multi-modal Logic-aware Retrieval](https://md.research.elamur.ai/free/en/papers/wu-2025-molorag-bootstrapping-document-understanding-multi/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xixi Wu, Yanchao Tan, Nan Hou, et al.*2025 · 12 citations*
15. **[Taming Language Models for Text-attributed Graph Learning with Decoupled Aggregation](https://md.research.elamur.ai/free/en/papers/zhou-2025-taming-language-models-text-attributed/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chuang Zhou, Zhu Wang, Shengyuan Chen, et al.*2025 · 9 citations*
16. [**You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures arXiv:2508.06105**Shengyuan Chen, Chuang Zhou, Zheng Yuan, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-don-t-need-pre-built/)
17. **[AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Construction](https://md.research.elamur.ai/free/en/papers/tsang-2026-autograph-r1-end-end-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hong Ting Tsang, Jiaxin Bai, Haoyu Huang, et al.*2026 · 4 citations*
18. **[Query-Efficient Agentic Graph Extraction Attacks on GraphRAG Systems](https://md.research.elamur.ai/free/en/papers/yang-2026-query-efficient-agentic-graph-extraction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuhua Yang, Jiahao Zhang, Yilong Wang, et al.*2026 · 4 citations*
19. **[What Breaks Knowledge Graph based RAG? Benchmarking and Empirical Insights into Reasoning under Incomplete Knowledge](https://md.research.elamur.ai/free/en/papers/zhou-2026-what-breaks-knowledge-graph-based/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Dongzhuoran Zhou, Yuqicheng Zhu, Xiaxia Wang, et al.*2026 · 3 citations*
20. **[Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation](https://md.research.elamur.ai/free/en/papers/wang-2026-exploring-graph-learning-tasks-pure/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxiang Wang, Xinnan Dai, Wenqi Fan, et al.*2026 · 1 citation*
21. **[QuALA-NL: Question & Answer with Legal Attribution in Dutch](https://md.research.elamur.ai/free/en/papers/drie-2026-quala-nl-question-answer-legal/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Romy A.N. van Drie, Roos M. Bakker, Daan L. Di Scala, et al.*2026 · 1 citation*
22. **[AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/sun-2026-agentgl-agentic-graph-learning-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuanfu Sun, Kang Li, Dongzhe Fan, et al.*2026*
23. **[Autonomous Knowledge Graph Exploration with Adaptive Breadth-Depth Retrieval](https://md.research.elamur.ai/free/en/papers/polonuer-2026-autonomous-knowledge-graph-exploration-adaptive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joaquin Polonuer, Lucas Vittor, Iñaki Arango, et al.*2026*
24. **[CITE: Benchmarking Heterogeneous Text-Attributed Graph Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-cite-benchmarking-heterogeneous-text-attributed/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chenghao Zhang, Qingqing Long, Ludi Wang, et al.*2026*
25. **[Collision to Cognition: Hash-Driven Graph Construction for Efficient RAG](https://md.research.elamur.ai/free/en/papers/zhou-2026-collision-cognition-hash-driven-graph/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chuang Zhou, Zheng Yuan, Linhao Luo, et al.*2026*
26. **[COSMOS: Connectivity-Oriented Submodular Maximization for Optimal Subgraph Retrieval](https://md.research.elamur.ai/free/en/papers/peng-2026-cosmos-connectivity-oriented-submodular-maximization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Boci Peng, Xiao Liu, Boren Hu, et al.*2026*
27. **[FlowRAG: Synergizing Explicit Reasoning via Frequency-Aware Multi-Granularity Graph Flow](https://md.research.elamur.ai/free/en/papers/zhan-2026-flowrag-synergizing-explicit-reasoning-frequency/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bihao Zhan, Zongsheng Cao, Jie Zhou, et al.*2026*
28. **[Follow the Path: Reasoning over Knowledge Graph Paths to Improve Large Language Model Factuality](https://md.research.elamur.ai/free/en/papers/zhang-2026-follow-path-reasoning-over-knowledge/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mike Zhang, Johannes Bjerva, Russa Biswas*2026*
29. **[Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment](https://md.research.elamur.ai/free/en/papers/zhang-2026-generalizable-llm-learning-graph-synthetic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yizhuo Zhang, Heng Wang, Shangbin Feng, et al.*2026*
30. **[Graph-Based Alternatives to LLMs for Human Simulation](https://md.research.elamur.ai/free/en/papers/suh-2026-graph-based-alternatives-llms-human/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joseph Suh, Suhong Moon, Serina Chang*2026*
31. **[HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering](https://md.research.elamur.ai/free/en/papers/shin-2026-hikey-hierarchical-multimodal-retrieval-open/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joongmin Shin, Gyuho Shim, Jeongbae Park, et al.*2026*
32. **[How to Make LMs Strong Node Classifiers?](https://md.research.elamur.ai/free/en/papers/xu-2026-how-make-lms-strong-node/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhe Xu, Kaveh Hassani, Si Zhang, et al.*2026*
33. **[HyperMem: Hypergraph Memory for Long-Term Conversations](https://md.research.elamur.ai/free/en/papers/yue-2026-hypermem-hypergraph-memory-long-term/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Juwei Yue, Chuanrui Hu, Jiawei Sheng, et al.*2026*
34. **[Interactive Semantic Parsing with Reinforcement Learning for Knowledge Graph Reasoning](https://md.research.elamur.ai/free/en/papers/song-2026-interactive-semantic-parsing-reinforcement-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yurun Song, Xiangqing Shen, Jianfei Yu, et al.*2026*
35. **[Mitigating Degree Bias in Hypergraphs via Attribute-as-Structure Approach](https://md.research.elamur.ai/free/en/papers/nishide-2026-mitigating-degree-bias-hypergraphs-attribute/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Ryusei Nishide, Makoto Miwa*2026*
36. **[MuSe: Multi-Stage Graph Reasoning via Vision-Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-muse-multi-stage-graph-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanyu Wang, Xu Chu, Zhijie Tan, et al.*2026*
37. **[NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering](https://md.research.elamur.ai/free/en/papers/shi-2026-ng-router-graph-supervised-multi/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Kaiwen Shi, Zheyuan Zhang, Zhengqing Yuan, et al.*2026*
38. **[Quantifying Retrieval Quality in GraphRAG: A Schema-Agnostic Approach](https://md.research.elamur.ai/free/en/papers/vanmechelen-2026-quantifying-retrieval-quality-graphrag-schema/) [Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26](https://md.research.elamur.ai/free/en/venues/kallm/)**Thibaud Vanmechelen, Alexandre Achten, Zaineb Gabsi, et al.*2026*
39. **[Query-Aware Graph Attention for Precise Subgraph Retrieval in Knowledge-Augmented Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2026-query-aware-graph-attention-precise/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuanye Xu, Linyi Guo, Yue Zhang, et al.*2026*
40. **[Query-Aware Knowledge Retrieval via Hyperbolic Structuring](https://md.research.elamur.ai/free/en/papers/zhou-2026-query-aware-knowledge-retrieval-hyperbolic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chuang Zhou, Junnan Dong, Yilin Xiao, et al.*2026*
41. [**Recent Advances in Text-attributed Graph Learning via Large Models (大模型驱动的文本属性图学习研究进展) Acta Automatica Sinica (自动化学报)**Yanping Zheng (郑艳萍), Jiarui Ji (季嘉蕊), Yuwei Hu (胡雨韦), et al.*2026*](https://md.research.elamur.ai/free/en/papers/zheng-2026-recent-advances-text/)
42. **[STEM: Structure-Tracing Evidence Mining for Knowledge Graphs-Driven Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/yu-2026-stem-structure-tracing-evidence-mining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peng Yu, En Xu, Bin Chen, et al.*2026*
43. **[Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering](https://md.research.elamur.ai/free/en/papers/tian-2026-subgraph-guided-executable-logical-form/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuhang Tian, Dandan Song, Zhijing Wu, et al.*2026*
44. **[The Structure-Content Trade-off in Knowledge Graph Retrieval: A Diagnostic Study of Question Decomposition](https://md.research.elamur.ai/free/en/papers/six-2026-structure-content-trade-off-knowledge/) [Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26](https://md.research.elamur.ai/free/en/venues/kallm/)**Valentin Six, Gaël de Chalendar, Evan Dufraisse*2026*
45. **[Think Parallax: Solving Multi-Hop Problems via Multi-View Knowledge-Graph-Based Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/liu-2026-think-parallax-solving-multi-hop/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinliang Liu, Jiale Bai, Shaoning Zeng*2026*
46. **[TopoRAG: Graph-based RAG via Topology-aware Approximate Nearest Neighbor Search](https://md.research.elamur.ai/free/en/papers/wu-2026-toporag-graph-based-rag-topology/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Tianhao Wu, Siqiang Luo*2026*
47. **[TRN-R1-Zero: Text-rich Network Reasoning via LLMs with Reinforcement Learning Only](https://md.research.elamur.ai/free/en/papers/liu-2026-trn-r1-zero-text-rich/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yilun Liu, Ruihong Qiu, Zi Huang*2026*

132 more citing papers of our library have no page here yet.

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