🔗 Who cites «Towards Effective and Efficient Continual Pre-training of Large Language Models»

20 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · ← back to «Towards Effective and Efficient Continual Pre-training of Large Language Models»

1. From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for TibetanAnnual Meeting of the Association for Computational Linguistics (ACL)Lei Yang, Leiyu Pan, Bojian Xiong, et al.2026 · 1 citation
2. Is Biomedical Specialization Still Worth It? Insights from Domain-Adaptive Language Modelling with a New French Health CorpusLanguage Resources and Evaluation Conference (LREC)Aidan Mannion, Cécile Macaire, Armand Violle, et al.2026
3. MUTANT: A Recipe for Multilingual Tokenizer DesignAnnual Meeting of the Association for Computational Linguistics (ACL)Souvik Rana, Ashish Kulkarni, Arul Menezes, et al.2026
4. Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-trainingAnnual Meeting of the Association for Computational Linguistics (ACL)Lei Liu, Hao Zhu, Xiaoyan Yang, et al.2026
5. Reading the News: Adapting Large Language Models to Swedish Journalism Through Continued Pre-TrainingarXiv:2608.30609Lukas Borggren, Jenny Kunz, Marco Kuhlmann2026
6. RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function LibraryFindings of the Association for Computational LinguisticsJiapeng Wang, Jinhao Jiang, Zhiqiang Zhang, et al.2026

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

This page in Markdown → https://md.research.elamur.ai/free/en/papers/chen-2025-effective-efficient-continual-pre-training/cited-by/