---
title: "Who cites Xuxi Chen — 141 papers of our library"
description: "141 papers of the Elamur Research library cite the works of Xuxi Chen, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/xuxi-chen/cited-by/"
---
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# Who cites Xuxi Chen

141 citations of their works from 141 papers of our library (of the 149,439 whose bibliographies we read) · 40 with a page here, the most cited first · [← Xuxi Chen](https://md.research.elamur.ai/free/en/authors/xuxi-chen/)

1. **[Instruction Tuning for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al.*2023 · 520 citations*
2. [**Large Language Models: A Survey arXiv:2402.06196**Shervin Minaee, Tomas Mikolov, Narjes Nikzad, et al.*2024 · 471 citations*](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/)
3. [**HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal arXiv:2402.04249**Mantas Mazeika, Long Phan, Xuwang Yin, et al.*2024 · 330 citations*](https://md.research.elamur.ai/free/en/papers/mazeika-2024-harmbench-standardized-evaluation-framework-automated/)
4. **[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*
5. **[Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs](https://md.research.elamur.ai/free/en/papers/ovadia-2024-fine-tuning-or-retrieval-comparing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Oded Ovadia, Menachem Brief, Moshik Mishaeli, et al.*2024 · 206 citations*
6. **[A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://md.research.elamur.ai/free/en/papers/wang-2024-comprehensive-survey-small-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.*2024 · 159 citations*
7. **[Scientific Large Language Models: A Survey on Biological & Chemical Domains](https://md.research.elamur.ai/free/en/papers/zhang-2024-scientific-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Qiang Zhang, Keyang Ding, Tianwen Lyv, et al.*2024 · 154 citations*
8. [**Orca-Math: Unlocking the potential of SLMs in Grade School Math arXiv:2402.14830**Arindam Mitra, Hamed Khanpour, Corby Rosset, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2024-orca-math-unlocking-potential-slms/)
9. [**Advancing LLM Reasoning Generalists with Preference Trees arXiv:2404.02078**Lifan Yuan, Ganqu Cui, Hanbin Wang, et al.*2024 · 94 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-advancing-llm-reasoning-generalists-preference/)
10. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
11. [**On Prompt-Driven Safeguarding for Large Language Models arXiv:2401.18018**Chujie Zheng, Fan Yin, Hao Zhou, et al.*2024 · 85 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2024-prompt-driven-safeguarding-large-language/)
12. [**Benchmarking Benchmark Leakage in Large Language Models arXiv:2404.18824**Ruijie Xu, Zengzhi Wang, Run-Ze Fan, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-benchmarking-benchmark-leakage-large-language/)
13. **[C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness](https://md.research.elamur.ai/free/en/papers/kang-2024-c3ot-generating-shorter-chain-thought/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yu Kang, Xianghui Sun, Liangyu Chen, et al.*2024 · 66 citations*
14. [**Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models arXiv:2402.13064**Haoran Li, Qingxiu Dong, Zhengyang Tang, et al.*2024 · 65 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-synthetic-data-almost-scratch-generalized/)
15. [**Phi-4-reasoning Technical Report arXiv:2504.21318**Marah Abdin, Sahaj Agarwal, Ahmed Awadallah, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2025-phi-4-reasoning-technical-report/)
16. [**RegMix: Data Mixture as Regression for Language Model Pre-training arXiv:2407.01492**Qian Liu, Xiaosen Zheng, Niklas Muennighoff, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-regmix-data-mixture-regression-language/)
17. [**TinyGSM: achieving >80% on GSM8k with small language models arXiv:2312.09241**Bingbin Liu, Sebastien Bubeck, Ronen Eldan, et al.*2023 · 45 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-tinygsm-achieving-80-gsm8k-small/)
18. [**Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences arXiv:2404.03715**Corby Rosset, Ching-An Cheng, Arindam Mitra, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/rosset-2024-direct-nash-optimization-teaching-language/)
19. [**AgentInstruct: Toward Generative Teaching with Agentic Flows arXiv:2407.03502**Arindam Mitra, Luciano Del Corro, Guoqing Zheng, et al.*2024 · 42 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2024-agentinstruct-toward-generative-teaching-agentic/)
20. **[Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models](https://md.research.elamur.ai/free/en/papers/patel-2024-multi-logieval-evaluating-multi-step/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nisarg Patel, Mohith Kulkarni, Mihir Parmar, et al.*2024 · 39 citations*
21. [**Functional Benchmarks for Robust Evaluation of Reasoning Performance, and the Reasoning Gap arXiv:2402.19450**Saurabh Srivastava, Annarose M B, Anto P, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2024-functional-benchmarks-robust-evaluation-reasoning/)
22. [**NExT: Teaching Large Language Models to Reason about Code Execution arXiv:2404.14662**Ansong Ni, Miltiadis Allamanis, Arman Cohan, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/ni-2024-next-teaching-large-language-models/)
23. **[LangBridge: Multilingual Reasoning Without Multilingual Supervision](https://md.research.elamur.ai/free/en/papers/yoon-2024-langbridge-multilingual-reasoning-without-multilingual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dongkeun Yoon, Joel Jang, Sungdong Kim, et al.*2024 · 29 citations*
24. **[CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation](https://md.research.elamur.ai/free/en/papers/ziegler-2025-craft-your-dataset-task-specific/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ingo Ziegler, Abdullatif Köksal, Desmond Elliott, et al.*2025 · 20 citations*
25. **[RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions](https://md.research.elamur.ai/free/en/papers/liu-2025-rag-instruct-boosting-llms-diverse/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wanlong Liu, Junying Chen, Ke Ji, et al.*2025 · 19 citations*
26. [**Towards Reasoning Ability of Small Language Models arXiv:2502.11569**Gaurav Srivastava, Shuxiang Cao, Xuan Wang*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2025-towards-reasoning-ability-small-language/)
27. **[What Happened in LLMs Layers when Trained for Fast vs. Slow Thinking: A Gradient Perspective](https://md.research.elamur.ai/free/en/papers/li-2025-what-happened-llms-layers-when/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ming Li, Yanhong Li, Tianyi Zhou*2025 · 15 citations*
28. [**Language Resources for Dutch Large Language Modelling arXiv:2312.12852**Bram Vanroy*2023 · 15 citations*](https://md.research.elamur.ai/free/en/papers/vanroy-2023-language-resources-dutch-large-language/)
29. **[Pre-training Distillation for Large Language Models: A Design Space Exploration](https://md.research.elamur.ai/free/en/papers/peng-2025-pre-training-distillation-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Peng, Xin Lv, Yushi Bai, et al.*2025 · 14 citations*
30. [**Knowledge Distillation of Black-Box Large Language Models arXiv:2401.07013**Hongzhan Chen, Ruijun Chen, Yuqi Yi, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-knowledge-distillation-black-box-large/)
31. [**Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization arXiv:2402.14270**Xuxi Chen, Zhendong Wang, Daouda Sow, et al.*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-take-bull-horns-hard-sample/)
32. **[Semantic Change Characterization with LLMs using Rhetorics](https://md.research.elamur.ai/free/en/papers/sa-2026-semantic-change-characterization-llms-rhetorics/) [Proceedings for the International Workshop on Computational Approaches to Language Change](https://md.research.elamur.ai/free/en/venues/lchange/)**Jáder Martins Camboim de Sá, Jooyoung Lee, Marcos Da Silveira, et al.*2026 · 3 citations*
33. **[Large Language Models for Citation Function Classification](https://md.research.elamur.ai/free/en/papers/vodicka-2026-large-language-models-citation-function/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Daniel Vodička, Pavel Kral, Christophe Cerisara, et al.*2026 · 1 citation*
34. **[Aligned Multi-View Scripts for Universal Chart-to-Code Generation](https://md.research.elamur.ai/free/en/papers/zhang-2026-aligned-multi-view-scripts-universal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhihan Zhang, Lizi Liao*2026*
35. **[DART: Mitigating Harm Drift in Difference-Aware LLMs via Distill-Audit-Repair Training](https://md.research.elamur.ai/free/en/papers/pan-2026-dart-mitigating-harm-drift-difference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ziwen Pan, Zihan Liang, Jad Kabbara, et al.*2026*
36. **[Extending Czech Aspect-Based Sentiment Analysis with Opinion Terms: Dataset and LLM Benchmarks](https://md.research.elamur.ai/free/en/papers/smid-2026-extending-czech-aspect-based-sentiment/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Jakub Šmíd, Pavel Priban, Pavel Kral*2026*
37. **[MARD: Module-Aware Reasoning Distillation for Language Models with Adaptive Supervision](https://md.research.elamur.ai/free/en/papers/yang-2026-mard-module-aware-reasoning-distillation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenqi Yang, Jianjun Li, Zhibo Zhang, et al.*2026*
38. **[SHARP: Self-adaptive Harmful Category-aware Prompt Generation for Black-box Jailbreaking](https://md.research.elamur.ai/free/en/papers/xue-2026-sharp-self-adaptive-harmful-category/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yingjie Xue, Xingyou Xia, Jun Zhang, et al.*2026*
39. **[The Challenge of Identifying the Origin of Black-Box Large Language Models](https://md.research.elamur.ai/free/en/papers/yang-2026-challenge-identifying-origin-black-box/) [Workshop on Privacy in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/privatenlp/)**Ziqing Yang, Yixin Wu, Yun Shen, et al.*2026*
40. **[TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-training](https://md.research.elamur.ai/free/en/papers/wang-2026-tikmix-efficient-semi-dynamic-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifan Wang, Binbinliu, Fengze Liu, et al.*2026*

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

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