🔗 Who cites «Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models»

49 papers of our library cite it (of the 141,840 whose bibliographies we read) · 6 with a page here, the most cited first · ← back to «Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models»

1. Crosslingual Generalization through Multitask FinetuningarXiv:2211.01786Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al.2022 · 695 citations
   Investigating Data Contamination in Modern Benchmarks for Large Language ModelsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Chunyuan Deng, Yilun Zhao, Xiangru Tang, et al.2024 · 137 citations
2. LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA CompositionarXiv:2307.13269Chengsong Huang, Qian Liu, Bill Yuchen Lin, et al.2023 · 102 citations
3. FLASK: Fine-grained Language Model Evaluation based on Alignment Skill SetsarXiv:2307.10928Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al.2023 · 94 citations
4. Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction TuningarXiv:2309.05444Ted Zadouri, Ahmet Ustun, Arash Ahmadian, et al.2023 · 66 citations
   Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular DataFindings of the Association for Computational LinguisticsMubashara Akhtar, Abhilash Shankarampeta, Vivek Gupta, et al.2023 · 38 citations

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

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