Who cites «Reviewer2: Optimizing Review Generation Through Prompt Generation»
- LLMs Assist NLP Researchers: Critique Paper (Meta-)ReviewingConference on Empirical Methods in Natural Language Processing (EMNLP)Jiangshu Du, Yibo Wang, Wenting Zhao, et al.2024 · 71 citations
- TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer ReviewConference on Empirical Methods in Natural Language Processing (EMNLP)Yuan Chang, Ziyue Li, Hengyuan Zhang, et al.2025 · 16 citations
- Mind the Blind Spots: A Focus-Level Evaluation Framework for LLM ReviewsConference on Empirical Methods in Natural Language Processing (EMNLP)Hyungyu Shin, Jingyu Tang, Yoonjoo Lee, et al.2025 · 14 citations
- Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research PapersAnnual Meeting of the Association for Computational Linguistics (ACL)Zhijian Xu, Yilun Zhao, Manasi Patwardhan, et al.2025 · 11 citations
- Reviewing Scientific Papers for Critical Problems With Reasoning LLMs: Baseline Approaches and Automatic EvaluationarXiv:2505.23824Tianmai M. Zhang, Neil F. Abernethy2025 · 7 citations
- Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response AssistanceAnnual Meeting of the Association for Computational Linguistics (ACL)Qianli Ma, Chang Guo, Zhiheng Tian, et al.2026 · 5 citations
- NovBench: Evaluating Large Language Models on Academic Paper Novelty AssessmentFindings of the Association for Computational LinguisticsWenqing Wu, Yi Zhao, Yuzhuo Wang, et al.2026 · 4 citations
- RbtAct: Rebuttal as Supervision for Actionable Review Feedback GenerationFindings of the Association for Computational LinguisticsSihong Wu, Yiling Ma, Yilun Zhao, et al.2026 · 2 citations
- Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the FutureAnnual Meeting of the Association for Computational Linguistics (ACL)Sihong Wu, Owen Jiang, Yilun Zhao, et al.2026 · 1 citation
This page in Markdown → https://md.research.elamur.ai/free/en/papers/gao-2024-reviewer2-optimizing-review-generation-through/cited-by/