---
title: "Who cites Large Language Model Instruction — 27 citing papers"
description: "27 papers of the Elamur Research library cite «Large Language Model Instruction Following: A Survey of Progresses and Challenges», found…"
canonical: "https://research.elamur.ai/free/en/papers/lou-2024-large-language-model-instruction-following/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/lou-2024-large-language-model-instruction-following/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Large Language Model Instruction Following: A…](https://md.research.elamur.ai/free/en/papers/lou-2024-large-language-model-instruction-following/) / cited by

# Who cites «Large Language Model Instruction Following: A Survey of Progresses and Challenges»

27 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 «Large Language Model Instruction Following: A Survey of Progresses and Challenges»](https://md.research.elamur.ai/free/en/papers/lou-2024-large-language-model-instruction-following/)

1. **[Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders](https://md.research.elamur.ai/free/en/papers/deng-2025-unveiling-language-specific-features-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Boyi Deng, Yu Wan, Baosong Yang, et al.*2025 · 18 citations*
2. **[OctoBench: Benchmarking Scaffold-Aware Instruction Following in Repository-Grounded Agentic Coding](https://md.research.elamur.ai/free/en/papers/ding-2026-octobench-benchmarking-scaffold-aware-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Deming Ding, Shichun Liu, Enhui Yang, et al.*2026 · 4 citations*
3. **[MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training](https://md.research.elamur.ai/free/en/papers/huang-2025-musc-improving-complex-instruction-following/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hui Huang, Jiaheng Liu, Yancheng He, et al.*2025 · 4 citations*
4. **[IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation](https://md.research.elamur.ai/free/en/papers/wen-2026-if-critic-fine-grained-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bosi Wen, Yilin Niu, Cunxiang Wang, et al.*2026 · 1 citation*
5. **[Seals-NLP at SemEval-2026 Task 9: A Comparative Study of Transformer Architectures for Polarization Detection](https://md.research.elamur.ai/free/en/papers/smith-2026-seals-nlp-semeval-2026-task/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Minh Smith, Cheryl Seals*2026 · 1 citation*
6. **[Mapping the Course for Prompt-based Structured Prediction](https://md.research.elamur.ai/free/en/papers/pauk-2026-mapping-course-prompt-based-structured/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Matt Pauk, Maria Leonor Pacheco*2026*
7. **[PARIF: Pushing the Pareto Frontier of Instruction Following and Reasoning with Curriculum Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/mu-2026-parif-pushing-pareto-frontier-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Rongchuan Mu, Zexin Wang, Qianyu Wang, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/lou-2024-large-language-model-instruction-following/) — Large Language Model Instruction Following: A Survey of Progresses and Challenges
