--- title: "Who cites Instruction Pre-Training: — 40 citing papers" description: "40 papers of the Elamur Research library cite «Instruction Pre-Training: Language Models are Supervised Multitask Learners», found in their…" canonical: "https://research.elamur.ai/free/en/papers/cheng-2024-instruction-pre-training-language-models/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/cheng-2024-instruction-pre-training-language-models/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Instruction Pre-Training: Language Models are…](https://md.research.elamur.ai/free/en/papers/cheng-2024-instruction-pre-training-language-models/) / cited by # Who cites «Instruction Pre-Training: Language Models are Supervised Multitask Learners» 40 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «Instruction Pre-Training: Language Models are Supervised Multitask Learners»](https://md.research.elamur.ai/free/en/papers/cheng-2024-instruction-pre-training-language-models/) 1. **[Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset](https://md.research.elamur.ai/free/en/papers/su-2025-nemotron-cc-transforming-common-crawl/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dan Su, Kezhi Kong, Ying Lin, et al.*2025 · 65 citations* 2. **[Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement](https://md.research.elamur.ai/free/en/papers/kong-2025-time-mqa-time-series-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaxuan Kong, Yiyuan Yang, Yoontae Hwang, et al.*2025 · 21 citations* 3. [**MiniPLM: Knowledge Distillation for Pre-Training Language Models arXiv:2410.17215**Yuxian Gu, Hao Zhou, Fandong Meng, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/gu-2024-miniplm-knowledge-distillation-pre-training/) 4. **[Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks](https://md.research.elamur.ai/free/en/papers/yamaguchi-2026-enhancing-linguistic-competence-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Atsuki Yamaguchi, Maggie Mi, Nikolaos Aletras*2026 · 1 citation* 5. **[FinCall-Surprise: A Large Scale Multi-modal Benchmark for Earning Surprise Prediction](https://md.research.elamur.ai/free/en/papers/shu-2026-fincall-surprise-large-scale-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dong Shu, Yanguang Liu, Huopu Zhang, et al.*2026 · 1 citation* 6. **[Tracing Multilingual Knowledge Acquisition Dynamics in Domain Adaptation: A Case Study of Biomedical Adaptation](https://md.research.elamur.ai/free/en/papers/zhao-2026-tracing-multilingual-knowledge-acquisition-dynamics/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Xin Zhao, Naoki Yoshinaga, Yuma Tsuta, et al.*2026 · 1 citation* 7. **[Is Micro Domain-Adaptive Pre-Training Effective for Real-World Operations? Multi-Step Evaluation Reveals Potential and Bottlenecks](https://md.research.elamur.ai/free/en/papers/tsunokake-2026-is-micro-domain-adaptive-pre/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Masaya Tsunokake, Yuta Koreeda, Terufumi Morishita, et al.*2026* 8. **[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* 9. **[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* 10. **[Merging Continual Pretraining Models for Domain-Specialized LLMs: A Case Study in Finance](https://md.research.elamur.ai/free/en/papers/ueda-2026-merging-continual-pretraining-models-domain/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Kentaro Ueda, François Portet, Hirohiko Suwa, et al.*2026* 11. **[PropGenie: A Multi-Agent Conversational Framework for Real Estate Assistance](https://md.research.elamur.ai/free/en/papers/shen-2026-propgenie-multi-agent-conversational-framework/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Chang Shen, Shaozu Yuan, Kuizong Wu, et al.*2026* 12. **[RADO: Reasoning Audit-Driven Optimization for Rigorous Reasoning in High-Stakes Domains](https://md.research.elamur.ai/free/en/papers/tan-2026-rado-reasoning-audit-driven-optimization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhijie Tan, Xu Chu, Guanyu Wang, et al.*2026* 13. [**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/) 27 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/cheng-2024-instruction-pre-training-language-models/) — Instruction Pre-Training: Language Models are Supervised Multitask Learners