---
title: "Who cites LoRAMoE: Alleviating World — 34 citing papers"
description: "34 papers of the Elamur Research library cite «LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style…"
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---
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# Who cites «LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin»

34 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin»](https://md.research.elamur.ai/free/en/papers/dou-2024-loramoe-alleviating-world-knowledge-forgetting/)

1. **[UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter Efficient Fine-Tuning of Large Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-uora-uniform-orthogonal-reinitialization-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xueyan Zhang, Jinman Zhao, Zhifei Yang, et al.*2025 · 14 citations*
2. **[Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric](https://md.research.elamur.ai/free/en/papers/yang-2025-measuring-data-diversity-instruction-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuming Yang, Yang Nan, Junjie Ye, et al.*2025 · 11 citations*
3. **[Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhao-2025-tiny-budgets-big-gains-parameter/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jinman Zhao, Xueyan Zhang, Jiaru Li, et al.*2025 · 8 citations*
4. **[PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation](https://md.research.elamur.ai/free/en/papers/zhang-2025-proper-progressive-learning-framework-personalized/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Linhai Zhang, Jialong Wu, Deyu Zhou, et al.*2025 · 6 citations*
5. **[Capability Decomposition for Unified Information Extraction via Hierarchical Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/zhou-2026-capability-decomposition-unified-information-extraction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jing Zhou, Peng Wang, Wenjun Ke, et al.*2026*
6. **[On the Representation Geometry of LoRA Model Merging](https://md.research.elamur.ai/free/en/papers/lu-2026-representation-geometry-lora-model-merging/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenyang Lu, Jiaru Li, Jinman Zhao, et al.*2026*
7. **[Specialization without Sparsity: Efficient and Expressive Split-Path Experts for LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/liu-2026-specialization-without-sparsity-efficient-expressive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ganghao Liu, Qin Zhou, Zhe Wang, et al.*2026*
8. **[TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models](https://md.research.elamur.ai/free/en/papers/mu-2026-talklora-communication-aware-mixture-low/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lin Mu, Haiyang Wang, Li Ni, et al.*2026*

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

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