---
title: "Who cites Shikun Zhang — 186 papers of our library"
description: "186 papers of the Elamur Research library cite the works of Shikun Zhang, found in their bibliographies; the most cited first."
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---
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# Who cites Shikun Zhang

186 citations of their works from 186 papers of our library (of the 149,439 whose bibliographies we read) · 50 with a page here, the most cited first · [← Shikun Zhang](https://md.research.elamur.ai/free/en/authors/shikun-zhang/)

1. **[Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://md.research.elamur.ai/free/en/papers/zheng-2023-judging-llm-judge-mt-bench/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, et al.*2023 · 4,573 citations*
2. **[A Survey on Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,648 citations*
3. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/)
4. [**ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate arXiv:2308.07201**Chi-Min Chan, Weize Chen, Yusheng Su, et al.*2023 · 359 citations*](https://md.research.elamur.ai/free/en/papers/chan-2023-chateval-better-llm-based/)
5. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
6. [**Prometheus: Inducing Fine-grained Evaluation Capability in Language Models arXiv:2310.08491**Seungone Kim, Jamin Shin, Yejin Cho, et al.*2023 · 281 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-prometheus-inducing-fine-grained-evaluation/)
7. **[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*
8. [**Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models arXiv:2405.01535**Seungone Kim, Juyoung Suk, Shayne Longpre, et al.*2024 · 234 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-prometheus-2-open-source-language/)
9. [**Aligning Large Language Models with Human: A Survey arXiv:2307.12966**Yufei Wang, Wanjun Zhong, Liangyou Li, et al.*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-aligning-large-language-models-human/)
10. [**LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods arXiv:2412.05579**Haitao Li, Qian Dong, Junjie Chen, et al.*2024 · 195 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llms-judges-comprehensive-survey-llm/)
11. **[LLM-based NLG Evaluation: Current Status and Challenges](https://md.research.elamur.ai/free/en/papers/gao-2025-llm-based-nlg-evaluation-current/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Mingqi Gao, Xinyu Hu, Xunjian Yin, et al.*2025 · 153 citations*
12. [**JudgeLM: Fine-tuned Large Language Models are Scalable Judges arXiv:2310.17631**Lianghui Zhu, Xinggang Wang, Xinlong Wang*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-judgelm-fine-tuned-large-language/)
13. **[InFoBench: Evaluating Instruction Following Ability in Large Language Models](https://md.research.elamur.ai/free/en/papers/qin-2024-infobench-evaluating-instruction-following-ability/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yiwei Qin, Kaiqiang Song, Yebowen Hu, et al.*2024 · 136 citations*
14. [**Generative Judge for Evaluating Alignment arXiv:2310.05470**Junlong Li, Shichao Sun, Weizhe Yuan, et al.*2023 · 125 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-generative-judge-evaluating-alignment/)
15. [**Large Language Model Alignment: A Survey arXiv:2309.15025**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
16. **[Style Over Substance: Evaluation Biases for Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2025-style-over-substance-evaluation-biases/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Minghao Wu, Alham Fikri Aji*2025 · 81 citations*
17. [**Wider and Deeper LLM Networks are Fairer LLM Evaluators arXiv:2308.01862**Xinghua Zhang, Bowen Yu, Haiyang Yu, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-wider-deeper-llm-networks-fairer/)
18. **[Black-Box Prompt Optimization: Aligning Large Language Models without Model Training](https://md.research.elamur.ai/free/en/papers/cheng-2024-black-box-prompt-optimization-aligning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiale Cheng, Xiao Liu, Kehan Zheng, et al.*2024 · 72 citations*
19. [**CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation arXiv:2311.18702**Pei Ke, Bosi Wen, Zhuoer Feng, et al.*2023 · 69 citations*](https://md.research.elamur.ai/free/en/papers/ke-2023-critiquellm-informative-critique-generation/)
20. **[OffsetBias: Leveraging Debiased Data for Tuning Evaluators](https://md.research.elamur.ai/free/en/papers/park-2024-offsetbias-leveraging-debiased-data-tuning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Junsoo Park, Seungyeon Jwa, Ren Meiying, et al.*2024 · 67 citations*
21. [**CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility arXiv:2307.09705**Guohai Xu, Jiayi Liu, Ming Yan, et al.*2023 · 64 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-cvalues-measuring-values-chinese/)
22. **[Leveraging Large Language Models for NLG Evaluation: Advances and Challenges](https://md.research.elamur.ai/free/en/papers/li-2024-leveraging-large-language-models-nlg/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Li, Xiaohan Xu, Tao Shen, et al.*2024 · 63 citations*
23. [**TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks arXiv:2310.00752**Dongfu Jiang, Yishan Li, Ge Zhang, et al.*2023 · 50 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-tigerscore-towards-building-explainable-metric/)
24. **[Direct Judgement Preference Optimization](https://md.research.elamur.ai/free/en/papers/wang-2025-direct-judgement-preference-optimization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**PeiFeng Wang, Austin Xu, Yilun Zhou, et al.*2025 · 35 citations*
25. [**Weaver: Foundation Models for Creative Writing arXiv:2401.17268**Tiannan Wang; Jiamin Chen; Qingrui Jia; Shuai Wang; Ruoyu Fang; Huilin Wang; Zhaowei Gao; Chunzhao Xie; Chuou Xu; Jihong Dai; Yibin Liu; Jialong Wu; Shengwei Ding; Long Li; Zhiwei Huang; Xinle Deng; Teng Yu; Gangan Ma; Han Xiao; Zixin Chen; Danjun Xiang; Yunxia Wang; Yuanyuan Zhu; Yi Xiao; Jing Wang; Yiru Wang; Siran Ding; Jiayang Huang; Jiayi Xu; Yilihamu Tayier; Zhenyu Hu; Yuan Gao; Chengfeng Zheng; Yueshu Ye; Yihang Li; Lei Wan; Xinyue Jiang; Yujie Wang; Siyu Cheng; Zhule Song; Xiangru Tang; Xiaohua Xu; Ningyu Zhang; Huajun Chen; Yuchen Eleanor Jiang;, Wangchunshu Zhou*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-weaver-foundation-models-creative-writing/)
26. [**NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens arXiv:2403.12766**Cunxiang Wang, Ruoxi Ning, Boqi Pan, et al.*2024 · 29 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-novelqa-benchmarking-question-answering-documents/)
27. **[Stable-RAG: Mitigating Retrieval-Permutation-Induced Hallucinations in Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/zhang-2026-stable-rag-mitigating-retrieval-permutation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qianchi Zhang, Hainan Zhang, Liang Pang, et al.*2026 · 27 citations*
28. [**FormalAlign: Automated Alignment Evaluation for Autoformalization arXiv:2410.10135**Jianqiao Lu, Yingjia Wan, Yinya Huang, et al.*2024 · 18 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-formalalign-automated-alignment-evaluation-autoformalization/)
29. **[CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward](https://md.research.elamur.ai/free/en/papers/liu-2025-compassverifier-unified-robust-verifier-llms/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shudong Liu, Hongwei Liu, Junnan Liu, et al.*2025 · 16 citations*
30. **[Rethinking Pragmatics in Large Language Models: Towards Open-Ended Evaluation and Preference Tuning](https://md.research.elamur.ai/free/en/papers/wu-2024-rethinking-pragmatics-large-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shengguang Wu, Shusheng Yang, Zhenglun Chen, et al.*2024 · 16 citations*
31. **[Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS](https://md.research.elamur.ai/free/en/papers/xu-2026-self-correcting-rag-enhancing-faithfulness/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shijia Xu, Zhou Wu, Xiaolong Jia, et al.*2026 · 15 citations*
32. [**From Calculation to Adjudication: Examining LLM judges on Mathematical Reasoning Tasks arXiv:2409.04168**Andreas Stephan, Dawei Zhu, Matthias Aßenmacher, et al.*2024 · 13 citations*](https://md.research.elamur.ai/free/en/papers/stephan-2024-calculation-adjudication-examining-llm-judges/)
33. **[RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game](https://md.research.elamur.ai/free/en/papers/xu-2026-rcbsf-multi-agent-framework-automated/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shijia Xu, Yu Wang, Xiaolong Jia, et al.*2026 · 11 citations*
34. **[Beyond the Surface: Measuring Self-Preference in LLM Judgments](https://md.research.elamur.ai/free/en/papers/chen-2025-beyond-surface-measuring-self-preference/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhi-Yuan Chen, Hao Wang, Xinyu Zhang, et al.*2025 · 10 citations*
35. [**Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding arXiv:2410.01671**Yanming Liu, Xinyue Peng, Jiannan Cao, et al.*2024 · 10 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-bridging-context-gaps-leveraging-coreference/)
36. **[Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study](https://md.research.elamur.ai/free/en/papers/song-2025-can-many-shot-context-learning/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Mingyang Song, Mao Zheng, Xuan Luo*2025 · 9 citations*
37. **[Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs](https://md.research.elamur.ai/free/en/papers/li-2025-self-instructed-derived-prompt-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhuo Li, Yuhao Du, Jinpeng Hu, et al.*2025 · 9 citations*
38. **[FinCARDS: Card-Based Analyst Reranking for Financial Document Question Answering](https://md.research.elamur.ai/free/en/papers/zhou-2026-fincards-card-based-analyst-reranking/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yixi Zhou, Fan Zhang, YU Chen, et al.*2026 · 8 citations*
39. [**Halu-J: Critique-Based Hallucination Judge arXiv:2407.12943**Binjie Wang, Steffi Chern, Ethan Chern, et al.*2024 · 8 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-halu-j-critique-based-hallucination/)
40. **[Dissecting Failure Dynamics in Large Language Model Reasoning](https://md.research.elamur.ai/free/en/papers/zhu-2026-dissecting-failure-dynamics-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wei Zhu, Jian Zhang, Lixing Yu, et al.*2026 · 6 citations*
41. [**mR3: Multilingual Rubric-Agnostic Reward Reasoning Models arXiv:2510.01146**David Anugraha, Shou-Yi Hung, Zilu Tang, et al.*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/anugraha-2025-mr3-multilingual-rubric-agnostic-reward/)
42. **[How Long Reasoning Chains Influence LLMs' Judgment of Answer Factuality](https://md.research.elamur.ai/free/en/papers/tu-2026-how-long-reasoning-chains-influence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minzhu Tu, Shiyu Ni, Keping Bi*2026 · 3 citations*
43. **[Graders Should Cheat: Privileged Information Enables Expert-Level Automated Evaluations](https://md.research.elamur.ai/free/en/papers/zhou-2025-graders-should-cheat-privileged-information/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jin Peng Zhou, Séb Arnold, Nan Ding, et al.*2025 · 3 citations*
44. **[C2-Faith: Benchmarking LLM Judges for Causal and Coverage Faithfulness in Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/mittal-2026-c2-faith-benchmarking-llm-judges/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Avni Mittal, Rauno Arike*2026 · 2 citations*
45. **[SafeConstellations: Mitigating Over-Refusals in LLMs Through Task-Aware Representation Steering](https://md.research.elamur.ai/free/en/papers/maskey-2026-safeconstellations-mitigating-over-refusals-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Utsav Maskey, Sumit Yadav, Mark Dras, et al.*2026 · 1 citation*
46. **[SCAN: Structured Capability Assessment and Navigation for LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-scan-structured-capability-assessment-navigation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zongqi Wang, Tianle Gu, Chen Gong, et al.*2026 · 1 citation*
47. **[JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation](https://md.research.elamur.ai/free/en/papers/shi-2026-judgeagent-beyond-static-benchmarks-knowledge/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhichao Shi, Xuhui Jiang, Chengjin Xu, et al.*2026*
48. **[Judging Instruction Responses in a Low-Resource Language: A Case Study on Basque](https://md.research.elamur.ai/free/en/papers/ponce-2026-judging-instruction-responses-low-resource/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**David Ponce, Harritxu Gete, Thierry Etchegoyhen, et al.*2026*
49. **[Poller: Are LLMs Suitable for Evaluating Poetry Understanding Task?](https://md.research.elamur.ai/free/en/papers/wang-2026-poller-are-llms-suitable-evaluating/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shanshan Wang, Derek F. Wong, Jingming Yao, et al.*2026*
50. **[Subgraph-Guided Executable Logical Form Generation for Knowledge Base Question Answering](https://md.research.elamur.ai/free/en/papers/tian-2026-subgraph-guided-executable-logical-form/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuhang Tian, Dandan Song, Zhijing Wu, et al.*2026*

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

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