---
title: "Who cites Gregor Geigle — 124 papers of our library"
description: "124 papers of the Elamur Research library cite the works of Gregor Geigle, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/gregor-geigle/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/gregor-geigle/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Gregor Geigle](https://md.research.elamur.ai/free/en/authors/gregor-geigle/) / cited by

# Who cites Gregor Geigle

124 citations of their works from 124 papers of our library (of the 149,439 whose bibliographies we read) · 27 with a page here, the most cited first · [← Gregor Geigle](https://md.research.elamur.ai/free/en/authors/gregor-geigle/)

1. [**LoRA: Low-Rank Adaptation of Large Language Models arXiv:2106.09685**Edward J. Hu, Yelong Shen, Phillip Wallis, et al.*2021 · 8,661 citations*](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/)
2. [**AdapterFusion: Non-Destructive Task Composition for Transfer Learning arXiv:2005.00247 · Proceedings of EACL 2021**Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, et al.*2020 · 958 citations*](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-adapterfusion-non-destructive-task-composition/)
3. **[MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-mad-x-adapter-based-framework/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jonas Pfeiffer, Ivan Vulić, Iryna Gurevych, et al.*2020 · 839 citations*
4. **[AdapterHub: A Framework for Adapting Transformers](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-adapterhub-framework-adapting-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, et al.*2020 · 754 citations*
5. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey arXiv:2403.14608**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/)
6. **[On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation](https://md.research.elamur.ai/free/en/papers/he-2021-effectiveness-adapter-based-tuning-pretrained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ruidan He, Linlin Liu, Hai Ye, et al.*2021 · 204 citations*
7. **[Lifting the Curse of Multilinguality by Pre-training Modular Transformers](https://md.research.elamur.ai/free/en/papers/pfeiffer-2022-lifting-curse-multilinguality-pre-training/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jonas Pfeiffer, Naman Goyal, Xi Victoria Lin, et al.*2022 · 174 citations*
8. **[Efficient Methods for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/treviso-2023-efficient-methods-natural-language-processing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marcos Treviso, Ji-Ung Lee, Tianchu Ji, et al.*2023 · 145 citations*
9. **[What to Pre-Train on? Efficient Intermediate Task Selection](https://md.research.elamur.ai/free/en/papers/poth-2021-what-pre-train-efficient-intermediate/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Clifton Poth, Jonas Pfeiffer, Andreas Rücklé, et al.*2021 · 132 citations*
10. **[MAD-G: Multilingual Adapter Generation for Efficient Cross-Lingual Transfer](https://md.research.elamur.ai/free/en/papers/ansell-2021-mad-g-multilingual-adapter-generation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alan Ansell, Edoardo Maria Ponti, Jonas Pfeiffer, et al.*2021 · 130 citations*
11. **[ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts](https://md.research.elamur.ai/free/en/papers/asai-2022-attempt-parameter-efficient-multi-task/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Akari Asai, Mohammadreza Salehi, Matthew Peters, et al.*2022 · 122 citations*
12. **[BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting](https://md.research.elamur.ai/free/en/papers/yong-2023-bloom-1-adding-language-support/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zheng Xin Yong, Hailey Schoelkopf, Niklas Muennighoff, et al.*2023 · 102 citations*
13. [**VeRA: Vector-based Random Matrix Adaptation arXiv:2310.11454**Dawid J. Kopiczko, Tijmen Blankevoort, Yuki M. Asano*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/kopiczko-2023-vera-vector-based-random-matrix/)
14. **[LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development](https://md.research.elamur.ai/free/en/papers/chalkidis-2023-lexfiles-legallama-facilitating-english-multinational/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ilias Chalkidis, Nicolas Garneau, Catalina Goanta, et al.*2023 · 83 citations*
15. **[FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual Models](https://md.research.elamur.ai/free/en/papers/dobler-2023-focus-effective-embedding-initialization-monolingual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Konstantin Dobler, Gerard de Melo*2023 · 62 citations*
16. **[AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhou-2024-autopeft-automatic-configuration-search-parameter/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Han Zhou, Xingchen Wan, Ivan Vulić, et al.*2024 · 46 citations*
17. **[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*
18. **[LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation](https://md.research.elamur.ai/free/en/papers/zhou-2025-lora-drop-efficient-lora-parameter/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Hongyun Zhou, Xiangyu Lu, Wang Xu, et al.*2025 · 12 citations*
19. **[Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model](https://md.research.elamur.ai/free/en/papers/xu-2025-let-s-focus-neuron-neuron/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Haoyun Xu, Runzhe Zhan, Yingpeng Ma, et al.*2025 · 10 citations*
20. **[Circuit Compositions: Exploring Modular Structures in Transformer-Based Language Models](https://md.research.elamur.ai/free/en/papers/mondorf-2025-circuit-compositions-exploring-modular-structures/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Philipp Mondorf, Sondre Wold, Barbara Plank*2025 · 8 citations*
21. **[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*
22. **[Fine-tuning Large Language Models with Limited Data: A Survey and Practical Guide](https://md.research.elamur.ai/free/en/papers/szep-2026-fine-tuning-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marton Szep, Daniel Rueckert, Rüdiger von Eisenhart-Rothe, et al.*2026 · 7 citations*
23. **[POLYCHARTQA: Benchmarking Large Vision-Language Models with Multilingual Chart Question Answering](https://md.research.elamur.ai/free/en/papers/xu-2026-polychartqa-benchmarking-large-vision-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yichen Xu, Liangyu Chen, Liang Zhang, et al.*2026 · 2 citations*
24. **[FormGym: Doing Paperwork with Agents](https://md.research.elamur.ai/free/en/papers/toles-2026-formgym-doing-paperwork-agents/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Matthew Toles, Isaac Song, Rattandeep Singh, et al.*2026*
25. **[Mind Your Special Tokens! On the Importance of Dedicated Sequence-End Tokens in Vision-Language Embedding Models](https://md.research.elamur.ai/free/en/papers/musacchio-2026-mind-your-special-tokens-importance/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Elio Musacchio, Giovanni Semeraro, Goran Glavaš*2026*
26. **[RanLoRA: Residual-aware Nonlinear Low-Rank Adaptation](https://md.research.elamur.ai/free/en/papers/luo-2026-ranlora-residual-aware-nonlinear-low/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xu Luo, Yongbin Liu, Chunping Ouyang, et al.*2026*
27. **[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*

97 more citing papers of our library have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/authors/gregor-geigle/) — Gregor Geigle