---
title: "Who cites Bahare Fatemi — 120 papers of our library"
description: "120 papers of the Elamur Research library cite the works of Bahare Fatemi, found in their bibliographies; the most cited first."
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---
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# Who cites Bahare Fatemi

126 citations of their works from 120 papers of our library (of the 149,439 whose bibliographies we read) · 37 with a page here, the most cited first · [← Bahare Fatemi](https://md.research.elamur.ai/free/en/authors/bahare-fatemi/)

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. **[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*
5. **[General-Reasoner: Advancing LLM Reasoning Across All Domains](https://md.research.elamur.ai/free/en/papers/ma-2025-general-reasoner-advancing-llm-reasoning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xueguang Ma, Qian Liu, Dongfu Jiang, et al.*2025 · 43 citations*
6. **[LocAgent: Graph-Guided LLM Agents for Code Localization](https://md.research.elamur.ai/free/en/papers/chen-2025-locagent-graph-guided-llm-agents/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhaoling Chen, Robert Tang, Gangda Deng, et al.*2025 · 27 citations*
7. [**Scaling Latent Reasoning via Looped Language Models arXiv:2510.25741**Rui-Jie Zhu, Zixuan Wang, Kai Hua, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-scaling-latent-reasoning-looped-language/)
8. **[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*
9. [**ERNIE 5.0 Technical Report arXiv:2602.04705**Haifeng Wang, Hua Wu, Tian Wu, et al.*2026 · 7 citations*](https://md.research.elamur.ai/free/en/papers/wang-2026-ernie-5-0-technical/)
10. **[Recitation over Reasoning: How Cutting-Edge Language Models Can Fail on Elementary School-Level Reasoning Problems?](https://md.research.elamur.ai/free/en/papers/yan-2025-recitation-over-reasoning-how-cutting/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Kai Yan, Yufei Xu, Zhengyin Du, et al.*2025 · 5 citations*
11. [**DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression arXiv:2609.19969**DeepSeek-AI, Anyi Xu, B. Li, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2026-deepseek-v4-1-flash/)
12. **[Instructions are all you need: Self-supervised Reinforcement Learning for Instruction Following](https://md.research.elamur.ai/free/en/papers/ren-2026-instructions-are-all-you-need/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingyu Ren, Qianyu He, Powei Chang, et al.*2026 · 3 citations*
13. **[J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization](https://md.research.elamur.ai/free/en/papers/xu-2026-j4r-learning-judge-equivalent-initial/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Austin Xu, Yilun Zhou, Xuan-Phi Nguyen, et al.*2026 · 3 citations*
14. **[SCALER: Synthetic Scalable Adaptive Learning Environment for Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2026-scaler-synthetic-scalable-adaptive-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Caijun Xu, Changyi Xiao, Zhongyuan Peng, et al.*2026 · 2 citations*
15. **[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*
16. **[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*
17. **[NeCCo: Nepali Cultural Commonsense Benchmark for Large Language Model Evaluation](https://md.research.elamur.ai/free/en/papers/shrestha-2026-necco-nepali-cultural-commonsense-benchmark/) [workshop on Challenges in Processing South Asian Languages](https://md.research.elamur.ai/free/en/venues/chipsal/)**Sanket Shrestha, Raunak Regmi, Sadikshya Ghimire, et al.*2026 · 1 citation*
18. **[When Do Large Language Models Exhibit Unsolicited Deception?](https://md.research.elamur.ai/free/en/papers/taylor-2026-when-do-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Samuel M. Taylor, Benjamin K. Bergen*2026 · 1 citation*
19. **[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*
20. **[BiMol-Diff: A Unified Diffusion Framework for Molecular Generation and Captioning](https://md.research.elamur.ai/free/en/papers/shahane-2026-bimol-diff-unified-diffusion-framework/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aditya Hemant Shahane, Anuj Kumar Sirohi, Devansh Arora, et al.*2026*
21. **[Can LLMs Really Judge? A Progressive Argumentation-Mining Framework for Distinguishing Understanding from Aggregation](https://md.research.elamur.ai/free/en/papers/wang-2026-can-llms-really-judge-progressive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Fuyu Wang, Jiangtong Li, Kun Zhu, et al.*2026*
22. **[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*
23. **[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*
24. **[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*
25. **[Graph-Assisted Large Language Models: A Perspective on Mitigating Intrinsic Limitations](https://md.research.elamur.ai/free/en/papers/luo-2026-graph-assisted-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haitong Luo, Fali Wang, Weiyao Zhang, et al.*2026*
26. **[GROKE: Vision-Free Navigation Instruction Evaluation via Graph Reasoning on OpenStreetMap](https://md.research.elamur.ai/free/en/papers/shami-2026-groke-vision-free-navigation-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Farzad Shami, Subhrasankha Dey, Nico Van de Weghe, et al.*2026*
27. **[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*
28. **[Linguistic Initialization for Inductive Reasoning in Heterogeneous Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/pasquini-2026-linguistic-initialization-inductive-reasoning-heterogeneous/) [Knowledge Graphs and Large Language Models Workshop (KG-LLM) @ LREC26](https://md.research.elamur.ai/free/en/venues/kallm/)**Daniele Pasquini, Danilo Croce, Roberto Basili*2026*
29. **[mSCoRe: A Multilingual and Scalable Benchmark for Skill-based Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/ngo-2026-mscore-multilingual-scalable-benchmark-skill/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Nghia Trung Ngo, Franck Dernoncourt, Thien Huu Nguyen*2026*
30. **[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*
31. **[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*
32. **[PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data](https://md.research.elamur.ai/free/en/papers/xiong-2026-puzzleclone-dsl-powered-framework-synthesizing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kai Xiong, Yanwei Huang, Rongjunchen Zhang, et al.*2026*
33. [**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/)
34. **[Routing with Generated Data: Annotation-Free LLM Skill Estimation and Expert Selection](https://md.research.elamur.ai/free/en/papers/niu-2026-routing-generated-data-annotation-free/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyi Niu, Justin Chen, Genta Indra Winata, et al.*2026*
35. **[SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs](https://md.research.elamur.ai/free/en/papers/zhao-2026-satquest-verifier-logical-reasoning-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yanxiao Zhao, Yaqian Li, Zihao Bo, et al.*2026*
36. **[Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement](https://md.research.elamur.ai/free/en/papers/zhong-2026-consistent-world-models-multi-token/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qimin Zhong, Hao Liao, Haiming Qin, et al.*2026*
37. **[UCS: Estimating Unseen Coverage for Improved In-Context Learning](https://md.research.elamur.ai/free/en/papers/xin-2026-ucs-estimating-unseen-coverage-improved/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiayi Xin, Xiang Li, Evan Qiang, et al.*2026*

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

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