---
title: "Who cites Can Language Models Solve — 86 citing papers"
description: "86 papers of the Elamur Research library cite «Can Language Models Solve Graph Problems in Natural Language?», found in their…"
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---
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# Who cites «Can Language Models Solve Graph Problems in Natural Language?»

86 papers of our library cite it (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · [← back to «Can Language Models Solve Graph Problems in Natural Language?»](https://md.research.elamur.ai/free/en/papers/wang-2023-language-models-solve-graph-problems/)

1. **[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*
2. **[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*
3. [**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/)
4. [**Talk like a Graph: Encoding Graphs for Large Language Models arXiv:2310.04560**Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/fatemi-2023-talk-like-graph-encoding-graphs/)
5. [**Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning arXiv:2305.19523**Xiaoxin He, Xavier Bresson, Thomas Laurent, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/he-2023-harnessing-explanations-llm-lm-interpreter/)
6. [**Resolving Knowledge Conflicts in Large Language Models arXiv:2310.00935**Yike Wang, Shangbin Feng, Heng Wang, et al.*2023 · 48 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-resolving-knowledge-conflicts-large-language/)
7. **[Can Graph Descriptive Order Affect Solving Graph Problems with LLMs?](https://md.research.elamur.ai/free/en/papers/ge-2025-can-graph-descriptive-order-affect/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuyao Ge, Shenghua Liu, Baolong Bi, et al.*2025 · 10 citations*
8. **[GraCoRe: Benchmarking Graph Comprehension and Complex Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/yuan-2025-gracore-benchmarking-graph-comprehension-complex/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Zike Yuan, Ming Liu, Hui Wang, et al.*2025 · 9 citations*
9. **[Agent-based Substructure Counting under Local Differential Privacy](https://md.research.elamur.ai/free/en/papers/zhang-2026-agent-based-substructure-counting-under/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuting Zhang, Kai Wang, Wei Ni, et al.*2026 · 1 citation*
10. **[Among Us: Measuring and Mitigating Malicious Contributions in Model Collaboration Systems](https://md.research.elamur.ai/free/en/papers/yang-2026-among-us-measuring-mitigating-malicious/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziyuan Yang, Wenxuan Ding, Shangbin Feng, et al.*2026 · 1 citation*
11. **[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*
12. **[Analyze Like a Venture Capitalist: Information-Gain and Knowledge Enhanced Graph Reasoning for Startup Success Prediction](https://md.research.elamur.ai/free/en/papers/pei-2026-analyze-like-venture-capitalist-information/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haoyu Pei, Zhongyang Liu, Xiangyi Xiao, et al.*2026*
13. **[Asking language models how to represent data for fine-tuning](https://md.research.elamur.ai/free/en/papers/singh-2026-asking-language-models-how-represent/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Usneek Singh, Ananya Singha, Abhijeet Awasthi, et al.*2026*
14. **[Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models](https://md.research.elamur.ai/free/en/papers/zangari-2026-colorful-talks-graphs-human-interpretable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Angelo Zangari, Peyman Baghershahi, Sourav Medya*2026*
15. **[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*
16. **[Generalization in Graph Reasoning: A Systematic Comparison of LLM Training Approaches](https://md.research.elamur.ai/free/en/papers/shirai-2026-generalization-graph-reasoning-systematic-comparison/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Sola Shirai, Kavitha Srinivas, Julian Dolby, et al.*2026*
17. **[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*
18. **[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*
19. **[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*
20. [**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/)
21. **[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*

65 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/wang-2023-language-models-solve-graph-problems/) — Can Language Models Solve Graph Problems in Natural Language?