---
title: "Who cites Minzhi Li — 147 papers of our library"
description: "147 papers of the Elamur Research library cite the works of Minzhi Li, found in their bibliographies; the most cited first."
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---
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# Who cites Minzhi Li

151 citations of their works from 147 papers of our library (of the 149,439 whose bibliographies we read) · 34 with a page here, the most cited first · [← Minzhi Li](https://md.research.elamur.ai/free/en/authors/minzhi-li/)

1. **[A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/fan-2024-survey-rag-meeting-llms-towards/) [ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/)**Wenqi Fan; Yujuan Ding; Liangbo Ning; Shijie Wang; Hengyun Li; Dawei Yin; Tat-Seng Chua;, Qing Li*2024 · 1,010 citations*
2. **[Can Large Language Models Transform Computational Social Science?](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Caleb Ziems, William Held, Omar Shaikh, et al.*2023 · 790 citations*
3. [**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/)
4. **[Large Language Models for Data Annotation and Synthesis: A Survey](https://md.research.elamur.ai/free/en/papers/tan-2024-large-language-models-data-annotation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Tan, Dawei Li, Song Wang, et al.*2024 · 262 citations*
5. **[On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://md.research.elamur.ai/free/en/papers/long-2024-llms-driven-synthetic-data-generation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lin Long, Rui Wang, Ruixuan Xiao, et al.*2024 · 237 citations*
6. **[Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://md.research.elamur.ai/free/en/papers/chen-2023-combating-misinformation-age-llms-opportunities/) [AI Magazine](https://md.research.elamur.ai/free/en/venues/ai-magazine/)**Canyu Chen, Kai Shu*2023 · 214 citations*
7. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
8. [**Adaptive Chameleon or Stubborn Sloth: Revealing the Behavior of Large Language Models in Knowledge Conflicts arXiv:2305.13300**Jian Xie, Kai Zhang, Jiangjie Chen, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/xie-2023-adaptive-chameleon-stubborn-sloth-revealing/)
9. **[Searching for Best Practices in Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/wang-2024-searching-best-practices-retrieval-augmented/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiaohua Wang, Zhenghua Wang, Xuan Gao, et al.*2024 · 150 citations*
10. [**Is GPT-3 a Good Data Annotator? arXiv:2212.10450**Bosheng Ding, Chengwei Qin, Linlin Liu, et al.*2022 · 147 citations*](https://md.research.elamur.ai/free/en/papers/ding-2022-gpt-3-good-data-annotator/)
11. [**Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models arXiv:2501.09686**Fengli Xu, Qianyue Hao, Zefang Zong, et al.*2025 · 120 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-towards-large-reasoning-models-survey/)
12. [**Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources arXiv:2305.13269**Xingxuan Li, Ruochen Zhao, Yew Ken Chia, et al.*2023 · 112 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-chain-knowledge-grounding-large-language/)
13. [**ColPali: Efficient Document Retrieval with Vision Language Models arXiv:2407.01449**Manuel Faysse, Hugues Sibille, Tony Wu, et al.*2024 · 96 citations*](https://md.research.elamur.ai/free/en/papers/faysse-2024-colpali-efficient-document-retrieval-vision/)
14. [**A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/)
15. **[VoiceBench: Benchmarking LLM-Based Voice Assistants](https://md.research.elamur.ai/free/en/papers/chen-2026-voicebench-benchmarking-llm-based-voice/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Yiming Chen, Xianghu Yue, Chen Zhang, et al.*2026 · 74 citations*
16. [**We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning? arXiv:2407.01284**Runqi Qiao, Qiuna Tan, Guanting Dong, et al.*2024 · 65 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2024-math-large-multimodal-model-achieve/)
17. [**RAT: Retrieval Augmented Thoughts Elicit Context-Aware Reasoning in Long-Horizon Generation arXiv:2403.05313**Zihao Wang, Anji Liu, Haowei Lin, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-rat-retrieval-augmented-thoughts-elicit/)
18. **[Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance](https://md.research.elamur.ai/free/en/papers/nahum-2025-are-llms-better-than-reported/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Omer Nahum, Nitay Calderon, Orgad Keller, et al.*2025 · 27 citations*
19. [**LLM-as-a-Judge & Reward Model: What They Can and Cannot Do arXiv:2409.11239**Guijin Son, Hyunwoo Ko, Hoyoung Lee, et al.*2024 · 27 citations*](https://md.research.elamur.ai/free/en/papers/son-2024-llm-judge-reward-model-they/)
20. **[Can Unconfident LLM Annotations Be Used for Confident Conclusions?](https://md.research.elamur.ai/free/en/papers/gligoric-2025-can-unconfident-llm-annotations-be/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kristina Gligorić, Tijana Zrnic, Cinoo Lee, et al.*2025 · 19 citations*
21. **[Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/yang-2025-holistic-evaluation-large-audio-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chih-Kai Yang, Neo S. Ho, Hung-yi Lee*2025 · 17 citations*
22. **[A Survey on Efficient Large Language Model Training: From Data-centric Perspectives](https://md.research.elamur.ai/free/en/papers/luo-2025-survey-efficient-large-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyu Luo, Bohan Wu, Xiao Luo, et al.*2025 · 14 citations*
23. **[Crisp: Cognitive Restructuring of Negative Thoughts through Multi-turn Supportive Dialogues](https://md.research.elamur.ai/free/en/papers/zhou-2025-crisp-cognitive-restructuring-negative-thoughts/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jinfeng Zhou, Yuxuan Chen, Jianing Yin, et al.*2025 · 10 citations*
24. **[WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback](https://md.research.elamur.ai/free/en/papers/shi-2026-wildfeedback-aligning-llms-situ-user/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Taiwei Shi, Zhuoer Wang, Longqi Yang, et al.*2026 · 9 citations*
25. **[Position: LLMs Can be Good Tutors in English Education](https://md.research.elamur.ai/free/en/papers/ye-2025-position-llms-can-be-good/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jingheng Ye, Shen Wang, Deqing Zou, et al.*2025 · 9 citations*
26. **[How About Kind of Generating Hedges using End-to-End Neural Models?](https://md.research.elamur.ai/free/en/papers/abulimiti-2023-how-about-kind-generating-hedges/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alafate Abulimiti, Chloé Clavel, Justine Cassell*2023 · 7 citations*
27. **[Audio Jailbreak: An Open Comprehensive Benchmark for Jailbreaking Large Audio-Language Models](https://md.research.elamur.ai/free/en/papers/song-2026-audio-jailbreak-open-comprehensive-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zirui Song, Qian Jiang, Mingxuan Cui, et al.*2026 · 3 citations*
28. **[AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluation](https://md.research.elamur.ai/free/en/papers/manakul-2026-audiojudge-understanding-what-works-large/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Potsawee Manakul, Woody Haosheng Gan, Michael J Ryan, et al.*2026 · 3 citations*
29. **[Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework](https://md.research.elamur.ai/free/en/papers/yan-2026-sculpting-vector-space-efficient-multi/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yibo Yan, Mingdong Ou, Yi Cao, et al.*2026 · 2 citations*
30. **[CARE-CR: Context-Aware Routing and Expert Fusion for Multi-Preference Cognitive Restructuring](https://md.research.elamur.ai/free/en/papers/qi-2026-care-cr-context-aware-routing/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hongzhi Qi, Liangcheng Wang, Yijing YU, et al.*2026*
31. **[Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents](https://md.research.elamur.ai/free/en/papers/ymyang-2026-chart-mrag-benchmarking-multimodal-retrieval/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ymyang, Jiang Zhong, Li Jin, et al.*2026*
32. **[DIFFA-2: A Practical Diffusion Large Language Model for General Audio Understanding](https://md.research.elamur.ai/free/en/papers/zhou-2026-diffa-2-practical-diffusion-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiaming Zhou, Xuxin Cheng, Shiwan Zhao, et al.*2026*
33. **[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*
34. **[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*

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

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