--- title: "Who cites DS2-Instruct: Domain-Specific — 2 citing papers" description: "2 papers of the Elamur Research library cite «DS2-Instruct: Domain-Specific Data Synthesis for Large Language Models Instruction Tuning»…" canonical: "https://research.elamur.ai/free/en/papers/xu-2026-ds2-instruct-domain-specific-data/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/xu-2026-ds2-instruct-domain-specific-data/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [DS2-Instruct: Domain-Specific Data Synthesis for…](https://md.research.elamur.ai/free/en/papers/xu-2026-ds2-instruct-domain-specific-data/) / cited by # Who cites «DS2-Instruct: Domain-Specific Data Synthesis for Large Language Models Instruction Tuning» 2 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «DS2-Instruct: Domain-Specific Data Synthesis for Large Language Models Instruction Tuning»](https://md.research.elamur.ai/free/en/papers/xu-2026-ds2-instruct-domain-specific-data/) 1. **[A Survey of Large Language Models for Text-Guided Molecular Discovery: From Molecule Generation to Optimization](https://md.research.elamur.ai/free/en/papers/wang-2026-survey-large-language-models-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziqing Wang, Kexin Zhang, Zihan Zhao, et al.*2026 · 7 citations* 2. **[CoAct: Co-Active LLM Preference Learning with Human-AI Synergy](https://md.research.elamur.ai/free/en/papers/xu-2026-coact-co-active-llm-preference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruiyao Xu, Mihir Parmar, Tiankai Yang, et al.*2026 · 1 citation* - [← back to](https://md.research.elamur.ai/free/en/papers/xu-2026-ds2-instruct-domain-specific-data/) — DS2-Instruct: Domain-Specific Data Synthesis for Large Language Models Instruction Tuning