🔗 Who cites «Through the Lens of Core Competency: Survey on Evaluation of Large Language Models»

24 papers of our library cite it (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · ← back to «Through the Lens of Core Competency: Survey on Evaluation of Large Language Models»

1. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,875 citations
2. Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language ModelsScience China Information SciencesQiguang Chen, Libo Qin, Jinhao Liu, et al.2025 · 176 citations
3. LLM-based NLG Evaluation: Current Status and ChallengesComputational LinguisticsMingqi Gao, Xinyu Hu, Xunjian Yin, et al.2025 · 153 citations
4. Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across LanguagesConference on Empirical Methods in Natural Language Processing (EMNLP)Libo Qin, Qiguang Chen, Fuxuan Wei, et al.2023 · 122 citations
5. A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and RecommendationsConference on Empirical Methods in Natural Language Processing (EMNLP)Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, et al.2024 · 105 citations
6. AlignBench: Benchmarking Chinese Alignment of Large Language ModelsarXiv:2311.18743Xiao Liu, Xuanyu Lei, Shengyuan Wang, et al.2023 · 74 citations
7. M³CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-ThoughtAnnual Meeting of the Association for Computational Linguistics (ACL)Qiguang Chen, Libo Qin, Jin Zhang, et al.2024 · 54 citations
8. AI4Research: A Survey of Artificial Intelligence for Scientific ResearchScience China Information SciencesQiguang Chen, Mingda Yang, Libo Qin, et al.2025 · 21 citations
9. What are the Essential Factors in Crafting Effective Long Context Multi-Hop Instruction Datasets? Insights and Best PracticesAnnual Meeting of the Association for Computational Linguistics (ACL)Zhi Chen, Qiguang Chen, Libo Qin, et al.2025 · 15 citations
10. ExpressivityBench: Can LLMs Communicate Implicitly?Findings of the Association for Computational LinguisticsJoshua Tint, Som Sagar, Aditya Taparia, et al.2026
11. Who Generates More Empathetic Responses–-Humans or LLMs? A Comparative Evaluation with Human and LLM JudgesConference on Computational Natural Language Learning (CoNLL)Anuradha Welivita, Fawzia Zeitoun, Pearl Pu2026

13 more papers of our library cite it; they have no page here yet.

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