---
title: "Who cites Han Zhou — 99 papers of our library"
description: "99 papers of the Elamur Research library cite the works of Han Zhou, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/han-zhou/cited-by/"
---
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# Who cites Han Zhou

101 citations of their works from 99 papers of our library (of the 149,439 whose bibliographies we read) · 24 with a page here, the most cited first · [← Han Zhou](https://md.research.elamur.ai/free/en/authors/han-zhou/)

1. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey arXiv:2403.14608**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/)
2. [**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/)
3. [**LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks arXiv:2406.18403**Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, et al.*2024 · 141 citations*](https://md.research.elamur.ai/free/en/papers/bavaresco-2024-llms-instead-human-judges-large/)
4. **[Survey of Cultural Awareness in Language Models: Text and Beyond](https://md.research.elamur.ai/free/en/papers/pawar-2025-survey-cultural-awareness-language-models/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Siddhesh Pawar, Junyeong Park, Jiho Jin, et al.*2025 · 106 citations*
5. **[Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://md.research.elamur.ai/free/en/papers/liu-2025-culturally-aware-adapted-nlp-taxonomy/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Chen Cecilia Liu, Iryna Gurevych, Anna Korhonen*2025 · 90 citations*
6. [**Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators arXiv:2403.16950**Yinhong Liu, Han Zhou, Zhijiang Guo, et al.*2024 · 76 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-aligning-human-judgement-role-pairwise/)
7. [**Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers arXiv:2404.04925**Libo Qin, Qiguang Chen, Yuhang Zhou, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-multilingual-large-language-model-survey/)
8. **[ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models](https://md.research.elamur.ai/free/en/papers/tan-2024-proxyqa-alternative-framework-evaluating-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haochen Tan, Zhijiang Guo, Zhan Shi, et al.*2024 · 30 citations*
9. [**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/)
10. [**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/)
11. **[Analyzing and Adapting Large Language Models for Few-Shot Multilingual NLU: Are We There Yet?](https://md.research.elamur.ai/free/en/papers/razumovskaia-2025-analyzing-adapting-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Evgeniia Razumovskaia, Ivan Vulić, Anna Korhonen*2025 · 15 citations*
12. **[CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges](https://md.research.elamur.ai/free/en/papers/li-2025-calibraeval-calibrating-prediction-distribution-mitigate/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haitao Li, Junjie Chen, Qingyao Ai, et al.*2025 · 15 citations*
13. **[Does Context Matter? ContextualJudgeBench for Evaluating LLM-based Judges in Contextual Settings](https://md.research.elamur.ai/free/en/papers/xu-2025-does-context-matter-contextualjudgebench-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Austin Xu, Srijan Bansal, Yifei Ming, et al.*2025 · 14 citations*
14. **[What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations](https://md.research.elamur.ai/free/en/papers/liu-2025-what-is-that-talk-about/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dongqi Liu, Chenxi Whitehouse, Xi Yu, et al.*2025 · 13 citations*
15. **[An Empirical Study of LLM-as-a-Judge: How Design Choices Impact Evaluation Reliability](https://md.research.elamur.ai/free/en/papers/yamauchi-2026-empirical-study-llm-as-judge/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Yusuke Yamauchi, Taro Yano, Masafumi Oyamada*2026 · 10 citations*
16. **[Sentient Agent as a Judge: Evaluating Higher-Order Social Cognition in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-sentient-agent-as-judge-evaluating/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bang Zhang, Ruotian Ma, Qingxuan Jiang, et al.*2026 · 6 citations*
17. **[Learning to Judge: LLMs Designing and Applying Evaluation Rubrics](https://md.research.elamur.ai/free/en/papers/siro-2026-learning-judge-llms-designing-applying/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Clemencia Siro, Pourya Aliannejadi, Mohammad Aliannejadi*2026 · 5 citations*
18. **[LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization](https://md.research.elamur.ai/free/en/papers/wu-2026-llm-prompt-duel-optimizer-efficient/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuanchen Wu, Saurabh Verma, Justin Lee, et al.*2026 · 2 citations*
19. [**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/)
20. **[Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling](https://md.research.elamur.ai/free/en/papers/wang-2026-aligning-agents-planning-benchmark-trajectory/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiaxuan Wang, Yulan Hu, Wenjin Yang, et al.*2026*
21. **[Evaluating Cost-Efficiency of LLMs in a RAG Setup on Polish Wikipedia: Quality vs. Energy Consumption](https://md.research.elamur.ai/free/en/papers/smits-2026-evaluating-cost-efficiency-llms-rag/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Patrycja Smits, Tomasz Walkowiak*2026*
22. **[Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders](https://md.research.elamur.ai/free/en/papers/zheng-2026-model-directions-not-words-mechanistic/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Carolina Zheng, Nicolas Beltran-Velez, Sweta Karlekar, et al.*2026*
23. **[RST-Guarder: Enhancing Long-Context Robustness for Safeguards via RST Parsing and Probabilistic Inference](https://md.research.elamur.ai/free/en/papers/zhang-2026-rst-guarder-enhancing-long-context/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xu Zhang, Xiaojun Wan*2026*
24. **[Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/schaffelder-2026-synthetic-eggs-many-baskets-impact/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Max Schaffelder, Albert Gatt*2026*

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

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