---
title: "Who cites Fuli Luo — 332 papers of our library — Elamur Research"
description: "332 papers of the Elamur Research library cite the works of Fuli Luo, found in their bibliographies; the most cited first."
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---
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# Who cites Fuli Luo

367 citations of their works from 332 papers of our library (of the 141,840 whose bibliographies we read) · 32 with a page here, the most cited first · [← Fuli Luo](https://md.research.elamur.ai/free/en/authors/fuli-luo/)

1. **[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://md.research.elamur.ai/free/en/papers/zheng-2024-llamafactory-unified-efficient-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaowei Zheng, Richong Zhang, Junhao Zhang, et al.*2024 · 1,035 citations*
2. [**Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement arXiv:2409.12122**An Yang, Beichen Zhang, Binyuan Hui, et al.*2024 · 410 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-qwen2-5-math-technical-report/)
3. [**A Survey on Large Language Models for Code Generation arXiv:2406.00515**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 366 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/)
4. [**The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery arXiv:2408.06292**Chris Lu, Cong Lu, Robert Tjarko Lange, et al.*2024 · 362 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-ai-scientist-fully-automated/)
5. [**Reformulating Unsupervised Style Transfer as Paraphrase Generation**Kalpesh Krishna, John Wieting, Mohit Iyyer*2020 · 348 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2020-reformulating-unsupervised-style-transfer-as/)
6. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey arXiv:2403.14608**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 236 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/)
7. [**RRHF: Rank Responses to Align Language Models with Human Feedback without tears arXiv:2304.05302**Zheng Yuan, Hongyi Yuan, Chuanqi Tan, et al.*2023 · 194 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-rrhf-rank-responses-align-language/)
8. [**From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline arXiv:2406.11939**Tianle Li, Wei-Lin Chiang, Evan Frick, et al.*2024 · 191 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-crowdsourced-data-high-quality-benchmarks/)
9. **[Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer](https://md.research.elamur.ai/free/en/papers/qi-2021-mind-style-text-adversarial-backdoor/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fanchao Qi, Yangyi Chen, Xurui Zhang, et al.*2021 · 188 citations*
10. [**Scaling Synthetic Data Creation with 1,000,000,000 Personas arXiv:2406.20094**Tao Ge, Xin Chan, Xiaoyang Wang, et al.*2024 · 160 citations*](https://md.research.elamur.ai/free/en/papers/ge-2024-scaling-synthetic-data-creation-1/)
11. [**InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency arXiv:2508.18265**Weiyun Wang, Zhangwei Gao, Lixin Gu, et al.*2025 · 159 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-internvl3-5-advancing-open/)
12. [**Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models arXiv:2410.07985**Bofei Gao, Feifan Song, Zhe Yang, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/gao-2024-omni-math-universal-olympiad-level/)
13. **[The Lessons of Developing Process Reward Models in Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/zhang-2025-lessons-developing-process-reward-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhenru Zhang, Chujie Zheng, Yangzhen Wu, et al.*2025 · 155 citations*
14. **[How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition](https://md.research.elamur.ai/free/en/papers/dong-2024-how-abilities-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanting Dong, Hongyi Yuan, Keming Lu, et al.*2024 · 145 citations*
15. [**Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing arXiv:2406.08464**Zhangchen Xu, Fengqing Jiang, Luyao Niu, et al.*2024 · 144 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-magpie-alignment-data-synthesis-scratch/)
16. [**A Recipe For Arbitrary Text Style Transfer with Large Language Models arXiv:2109.03910**Emily Reif, Daphne Ippolito, Ann Yuan, et al.*2021 · 126 citations*](https://md.research.elamur.ai/free/en/papers/reif-2021-recipe-arbitrary-text-style-transfer/)
17. **[ParaDetox: Detoxification with Parallel Data](https://md.research.elamur.ai/free/en/papers/logacheva-2022-paradetox-detoxification-parallel-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Varvara Logacheva, Daryna Dementieva, Sergey Ustyantsev, et al.*2022 · 120 citations*
18. [**DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence arXiv:2606.19348**DeepSeek-AI, Anyi Xu, Bangcai Lin, et al.*2026 · 117 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2026-deepseek-v4-highly-efficient/)
19. [**DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search arXiv:2408.08152**Huajian Xin, Z. Z. Ren, Junxiao Song, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/xin-2024-deepseek-prover-v1-5-harnessing/)
20. **[LENS: A Learnable Evaluation Metric for Text Simplification](https://md.research.elamur.ai/free/en/papers/maddela-2023-lens-learnable-evaluation-metric-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mounica Maddela, Yao Dou, David Heineman, et al.*2023 · 103 citations*
21. **[OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models](https://md.research.elamur.ai/free/en/papers/huang-2025-opencoder-open-cookbook-top-tier/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Siming Huang, Tianhao Cheng, Jason Klein Liu, et al.*2025 · 81 citations*
22. [**Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer**Eleftheria Briakou, Sweta Agrawal, Joel Tetreault, et al.*2021 · 49 citations*](https://md.research.elamur.ai/free/en/papers/briakou-2021-evaluating-evaluation-metrics-style-transfer/)
23. [**AMR-TST: Abstract Meaning Representation-based Text Style Transfer**Kaize Shi, Xueyao Sun, Li He, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-amr-tst-abstract-meaning-representation/)
24. [**T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation**Anubhav Jangra, Preksha Nema, Aravindan Raghuveer*2022 · 11 citations*](https://md.research.elamur.ai/free/en/papers/jangra-2022-t-star-truthful-style-transfer/)
25. [**A Neural Approach to Irony Generation arXiv:1909.06200**Mengdi Zhu, Zhiwei Yu, Xiaojun Wan*2019 · 9 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2019-neural-approach-irony-generation/)
26. [**SemEval-2022 Task 3: PreTENS — Evaluating Neural Networks on Presuppositional Semantic Knowledge**Roberto Zamparelli, Shammur Chowdhury, Dominique Brunato, et al.*2022 · 8 citations*](https://md.research.elamur.ai/free/en/papers/zamparelli-2022-semeval-2022-task-3-pretens/)
27. [**Acquiring a Formality-Informed Lexical Resource for Style Analysis**Elisabeth Eder, Ulrike Krieg-Holz, Udo Hahn*2021 · 6 citations*](https://md.research.elamur.ai/free/en/papers/eder-2021-acquiring-formality-informed-lexical-resource/)
28. **[Scaling Laws for Code: A More Data-Hungry Regime](https://md.research.elamur.ai/free/en/papers/luo-2026-scaling-laws-code-more-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xianzhen Luo, Wenzhen Zheng, Qingfu Zhu, et al.*2026 · 3 citations*
29. **[ReproHum #0669-08: Reproducing a Recipe for Arbitrary Text Style Transfer with LLMs](https://md.research.elamur.ai/free/en/papers/mahamood-2026-reprohum-0669-08-reproducing-recipe/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Saad Mahamood*2026 · 1 citation*
30. [**A Survey on Parameter-efficient Fine-tuning of Large Models: Techniques, Trends, and Challenges (大模型参数高效微调方法综述: 技术、趋势与挑战) Acta Automatica Sinica (自动化学报)**Anda Tang (唐岸达), Zhouchen Lin (林宙辰)*2026*](https://md.research.elamur.ai/free/en/papers/tang-2026-parameter-efficient-fine/)
31. **[Diff4TST: Masked Diffusion Language Model for Text Style Transfer](https://md.research.elamur.ai/free/en/papers/ma-2026-diff4tst-masked-diffusion-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinchen Ma, Gaole He, Yunshi Lan, et al.*2026*
32. [**Survey of Tool Use in Large Language Models (大语言模型的工具使用综述) Acta Automatica Sinica (自动化学报)**Yining Zheng (郑逸宁), Zhen Yu (余镇), Bufan Li (李不凡), et al.*2025*](https://md.research.elamur.ai/free/en/papers/zheng-2025-tool-use-large/)

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

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