🔗 Who cites «Entropy Law: The Story Behind Data Compression and LLM Performance»

16 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · ← back to «Entropy Law: The Story Behind Data Compression and LLM Performance»

1. Pangu Embedded: An Efficient Dual-system LLM Reasoner with MetacognitionarXiv:2505.22375Hanting Chen, Yasheng Wang, Kai Han, et al.2025 · 13 citations
2. Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum LearningConference of the European Chapter of the Association for Computational Linguistics (EACL)Yang Zhang, Amr Mohamed, Hadi Abdine, et al.2026 · 7 citations
3. Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUsarXiv:2504.07866Yichun Yin, Wenyong Huang, Kaikai Song, et al.2025 · 6 citations
4. Pangu Pro MoE: Mixture of Grouped Experts for Efficient SparsityarXiv:2505.21411Yehui Tang, Xiaosong Li, Fangcheng Liu, et al.2025 · 4 citations
5. Concept rather than Document: Context Compression via AMR-based Conceptual EntropyFindings of the Association for Computational LinguisticsKaize Shi, Xueyao Sun, Xiaohui Tao, et al.2026
6. From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Ling Shi, Xinwei Wu, Xiaohu Zhao, et al.2026
7. InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-TuningAnnual Meeting of the Association for Computational Linguistics (ACL)Junyou Su, He Zhu, Xiao Luo, et al.2026

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

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