--- title: "Who cites LaMini-LM: A Diverse Herd of — 81 citing papers" description: "81 papers of the Elamur Research library cite «LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions», found in their…" canonical: "https://research.elamur.ai/free/en/papers/wu-2023-lamini-lm-diverse-herd-distilled/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wu-2023-lamini-lm-diverse-herd-distilled/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [LaMini-LM: A Diverse Herd of Distilled Models…](https://md.research.elamur.ai/free/en/papers/wu-2023-lamini-lm-diverse-herd-distilled/) / cited by # Who cites «LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions» 81 papers of our library cite it (of the 149,439 whose bibliographies we read) · 19 with a page here, the most cited first · [← back to «LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions»](https://md.research.elamur.ai/free/en/papers/wu-2023-lamini-lm-diverse-herd-distilled/) 1. **[LLM-Pruner: On the Structural Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/ma-2023-llm-pruner-structural-pruning-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xinyin Ma, Gongfan Fang, Xinchao Wang*2023 · 452 citations* 2. [**CMMLU: Measuring massive multitask language understanding in Chinese arXiv:2306.09212**Haonan Li, Yixuan Zhang, Fajri Koto, et al.*2023 · 387 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-cmmlu-measuring-massive-multitask-language/) 3. **[A Survey on Model Compression for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2024-survey-model-compression-large-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Xunyu Zhu, Jian Li, Yong Liu, et al.*2024 · 381 citations* 4. [**Aligning Large Language Models with Human: A Survey arXiv:2307.12966**Yufei Wang, Wanjun Zhong, Liangyou Li, et al.*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-aligning-large-language-models-human/) 5. **[A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-safety-trustworthiness-large-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Xiaowei Huang, Wenjie Ruan, Wei Huang, et al.*2023 · 199 citations* 6. **[Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback](https://md.research.elamur.ai/free/en/papers/lai-2023-okapi-instruction-tuned-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Viet Lai, Chien Nguyen, Nghia Ngo, et al.*2023 · 195 citations* 7. [**MiniLLM: On-Policy Distillation of Large Language Models arXiv:2306.08543**Yuxian Gu, Li Dong, Furu Wei, et al.*2023 · 144 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/) 8. [**Platypus: Quick, Cheap, and Powerful Refinement of LLMs arXiv:2308.07317**Ariel N. Lee, Cole J. Hunter, Nataniel Ruiz*2023 · 117 citations*](https://md.research.elamur.ai/free/en/papers/lee-2023-platypus-quick-cheap-powerful-refinement/) 9. [**A Survey on Efficient Inference for Large Language Models arXiv:2404.14294**Zixuan Zhou, Xuefei Ning, Ke Hong, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-survey-efficient-inference-large-language/) 10. **[GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP](https://md.research.elamur.ai/free/en/papers/khondaker-2023-gptaraeval-comprehensive-evaluation-chatgpt-arabic/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Md Tawkat Islam Khondaker, Abdul Waheed, El Moatez Billah Nagoudi, et al.*2023 · 110 citations* 11. [**Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation arXiv:2305.15011**Haonan Li, Fajri Koto, Minghao Wu, et al.*2023 · 96 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-bactrian-x-multilingual-replicable-instruction/) 12. [**LLM Inference Unveiled: Survey and Roofline Model Insights arXiv 2024**Zhihang Yuan, Yuzhang Shang, Yang Zhou, et al.*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2024-llm-inference-unveiled-survey-roofline/) 13. **[Style Over Substance: Evaluation Biases for Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2025-style-over-substance-evaluation-biases/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Minghao Wu, Alham Fikri Aji*2025 · 81 citations* 14. [**Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration arXiv:2306.09093**Chenyang Lyu, Minghao Wu, Longyue Wang, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/lyu-2023-macaw-llm-multi-modal-language/) 15. [**Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs arXiv:2402.12030 · Transactions on Machine Learning Research, January 2025**Nicolas Boizard, Kevin El Haddad, Celine Hudelot, et al.*2024 · 35 citations*](https://md.research.elamur.ai/free/en/papers/boizard-2024-towards-cross-tokenizer-distillation-universal/) 16. **[Hala Technical Report Building Arabic-Centric Instruction & Translation Models at Scale](https://md.research.elamur.ai/free/en/papers/hammoud-2026-hala-technical-report-building-arabic/) [Workshop on NLP for Languages Using Arabic Script](https://md.research.elamur.ai/free/en/venues/abjadnlp/)**Hasan Abed Al Kader Hammoud, Mohamad Bilal Zbib, Bernard Ghanem*2026 · 5 citations* 17. **[KNN-SSD: Enabling Dynamic Self-Speculative Decoding via Nearest Neighbor Layer Set Optimization](https://md.research.elamur.ai/free/en/papers/song-2026-knn-ssd-enabling-dynamic-self/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mingbo Song, Heming Xia, Jun Zhang, et al.*2026 · 5 citations* 18. **[CodeGuard: Improving LLM Guardrails in CS Education](https://md.research.elamur.ai/free/en/papers/raihan-2026-codeguard-improving-llm-guardrails-cs/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nishat Raihan, Noah Erdachew, Jayoti Devi, et al.*2026* 19. **[Synthetic Data Generation for Training Diversified Commonsense Reasoning Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-synthetic-data-generation-training-diversified/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianhui Zhang, Bei Peng, Danushka Bollegala*2026* 62 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/wu-2023-lamini-lm-diverse-herd-distilled/) — LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions