---
title: "Who cites Towards Effective and Efficient — 20 citing papers"
description: "20 papers of the Elamur Research library cite «Towards Effective and Efficient Continual Pre-training of Large Language Models», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/chen-2025-effective-efficient-continual-pre-training/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/chen-2025-effective-efficient-continual-pre-training/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Towards Effective and Efficient Continual…](https://md.research.elamur.ai/free/en/papers/chen-2025-effective-efficient-continual-pre-training/) / cited by

# 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) · 8 with a page here, the most cited first · [← back to «Towards Effective and Efficient Continual Pre-training of Large Language Models»](https://md.research.elamur.ai/free/en/papers/chen-2025-effective-efficient-continual-pre-training/)

1. **[From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan](https://md.research.elamur.ai/free/en/papers/yang-2026-curated-data-scalable-models-continual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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 Corpus](https://md.research.elamur.ai/free/en/papers/mannion-2026-is-biomedical-specialization-still-worth/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Aidan Mannion, Cécile Macaire, Armand Violle, et al.*2026*
3. **[Large-Scale Diverse Synthesis for Mid-Training](https://md.research.elamur.ai/free/en/papers/zhang-2026-large-scale-diverse-synthesis-mid/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xuemiao Zhang, Chengying Tu, Can Ren, et al.*2026*
4. **[LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge Points](https://md.research.elamur.ai/free/en/papers/zhang-2026-linkqa-synthesizing-diverse-qa-multiple/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xuemiao Zhang, Can Ren, Chengying Tu, et al.*2026*
5. **[MUTANT: A Recipe for Multilingual Tokenizer Design](https://md.research.elamur.ai/free/en/papers/rana-2026-mutant-recipe-multilingual-tokenizer-design/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Souvik Rana, Ashish Kulkarni, Arul Menezes, et al.*2026*
6. **[Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training](https://md.research.elamur.ai/free/en/papers/liu-2026-perplexity-aware-data-scaling-law/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lei Liu, Hao Zhu, Xiaoyan Yang, et al.*2026*
7. [**Reading the News: Adapting Large Language Models to Swedish Journalism Through Continued Pre-Training arXiv:2608.30609**Lukas Borggren, Jenny Kunz, Marco Kuhlmann*2026*](https://md.research.elamur.ai/free/en/papers/borggren-2026-reading-news-adapting-large-language/)
8. **[RV-Syn: Rational and Verifiable Mathematical Reasoning Data Synthesis based on Structured Function Library](https://md.research.elamur.ai/free/en/papers/wang-2026-rv-syn-rational-verifiable-mathematical/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiapeng Wang, Jinhao Jiang, Zhiqiang Zhang, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/chen-2025-effective-efficient-continual-pre-training/) — Towards Effective and Efficient Continual Pre-training of Large Language Models