🧑‍🔬 Who cites Zhicheng Guo

67 citations of their works from 67 papers of our library (of the 149,439 whose bibliographies we read) · 18 with a page here, the most cited first · ← Zhicheng Guo

1. Large Language Models for Data Annotation and Synthesis: A SurveyConference on Empirical Methods in Natural Language Processing (EMNLP)Zhen Tan, Dawei Li, Song Wang, et al.2024 · 262 citations
2. When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMsTransactions of the Association for Computational Linguistics (TACL)Ryo Kamoi, Yusen Zhang, Nan Zhang, et al.2024 · 161 citations
3. Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction StrategiesTransactions of the Association for Computational Linguistics (TACL)Liangming Pan, Michael Saxon, Wenda Xu, et al.2024 · 140 citations
4. LLMs cannot find reasoning errors, but can correct them given the error locationFindings of the Association for Computational LinguisticsGladys Tyen, Hassan Mansoor, Victor Carbune, et al.2024 · 85 citations
5. Meta-Prompting: Enhancing Language Models with Task-Agnostic ScaffoldingarXiv:2401.12954Mirac Suzgun, Adam Tauman Kalai2024 · 62 citations
6. Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form TextsConference on Machine Translation (WMT)Eleftheria Briakou, Jiaming Luo, Colin Cherry, et al.2024 · 46 citations
7. Do Large Language Models have an English Accent? Evaluating and Improving the Naturalness of Multilingual LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Yanzhu Guo, Simone Conia, Zelin Zhou, et al.2025 · 25 citations
8. MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation EvaluatorsInternational Conference on Computational Linguistics (COLING)Qingyu Lu, Liang Ding, Kanjian Zhang, et al.2025 · 22 citations
9. A Survey of Post-Training Scaling in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Hanyu Lai, Xiao Liu, Junjie Gao, et al.2025 · 18 citations
10. Benchmarking LLMs for Translating Classical Chinese Poetry: Evaluating Adequacy, Fluency, and EleganceConference on Empirical Methods in Natural Language Processing (EMNLP)Andong Chen, Lianzhang Lou, Kehai Chen, et al.2025 · 18 citations
11. Lost in Literalism: How Supervised Training Shapes Translationese in LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Yafu Li, Ronghao Zhang, Zhilin Wang, et al.2025 · 12 citations
12. Please Translate Again: Two Simple Experiments on Whether Human-Like Reasoning Helps TranslationConference on Empirical Methods in Natural Language Processing (EMNLP)Di Wu, Seth Aycock, Christof Monz2025 · 12 citations
13. Decision-Making with Deliberation: Meta-reviewing as a Document-grounded DialogueConference of the European Chapter of the Association for Computational Linguistics (EACL)Sukannya Purkayastha, Nils Dycke, Anne Lauscher, et al.2026 · 2 citations
14. BlAInded by Fluency: How Idiomatic Machine Translation Outputs Affect Student Post-Editors' Edit TypesAnnual Conference of the European Association for Machine Translation (EAMT)Valentin Scourneau, Loïc De Faria Pires2026
15. Diagnose, Then Repair: A Two-Stage MQM-Guided Post-Editing Framework for Domain-Specific Machine TranslationAnnual Meeting of the Association for Computational Linguistics (ACL)Ji Hun Wang, Siyu Wu2026
16. FeedTrans: Enhance Machine Translation via FeedbackTransactions of the Association for Computational Linguistics (TACL)Zengkui Sun, Jiali Zeng, Jiaan Wang, et al.2026
17. Predict and Fix: A Unified Model for Translation Quality Estimation and Post-EditingConference of the Association for Machine Translation in the Americas (AMTA)Maciej Modrzejewski, Yash Bhaskar, Chinmay Pateria2026
18. What Does LLM Refinement Actually Improve? A Systematic Study on Document-Level Literary TranslationAnnual Meeting of the Association for Computational Linguistics (ACL)Shaomu Tan, Dawei Zhu, Ke Tran, et al.2026

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