---
title: "Who cites Clifton Poth — 331 papers of our library"
description: "331 papers of the Elamur Research library cite the works of Clifton Poth, found in their bibliographies; the most cited first."
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---
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# Who cites Clifton Poth

349 citations of their works from 331 papers of our library (of the 141,840 whose bibliographies we read) · 30 with a page here, the most cited first · [← Clifton Poth](https://md.research.elamur.ai/free/en/authors/clifton-poth/)

1. **[Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,552 citations*
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 · 950 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 · 833 citations*
4. **[How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models](https://md.research.elamur.ai/free/en/papers/rust-2021-how-good-is-your-tokenizer/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Phillip Rust, Jonas Pfeiffer, Ivan Vulić, et al.*2021 · 444 citations*
5. **[TIES-Merging: Resolving Interference When Merging Models](https://md.research.elamur.ai/free/en/papers/yadav-2023-ties-merging-resolving-interference-merging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Prateek Yadav, Derek Tam, Leshem Choshen, et al.*2023 · 308 citations*
6. **[SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://md.research.elamur.ai/free/en/papers/vu-2022-spot-better-frozen-model-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tu Vu, Brian Lester, Noah Constant, et al.*2022 · 302 citations*
7. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 300 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
8. **[AdapterDrop: On the Efficiency of Adapters in Transformers](https://md.research.elamur.ai/free/en/papers/ruckle-2021-adapterdrop-efficiency-adapters-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Andreas Rücklé, Gregor Geigle, Max Glockner, et al.*2021 · 295 citations*
9. **[Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks](https://md.research.elamur.ai/free/en/papers/mahabadi-2021-parameter-efficient-multi-task-fine/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani, et al.*2021 · 250 citations*
10. **[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*
11. **[UNKs Everywhere: Adapting Multilingual Language Models to New Scripts](https://md.research.elamur.ai/free/en/papers/pfeiffer-2021-unks-everywhere-adapting-multilingual-language/) [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.*2021 · 175 citations*
12. **[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 · 172 citations*
13. [**Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing**Minh Van Nguyen, Viet Dac Lai, Amir Pouran Ben Veyseh, et al.*2021 · 169 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2021-trankit-light-weight-transformer-based/)
14. **[Sustainable Modular Debiasing of Language Models](https://md.research.elamur.ai/free/en/papers/lauscher-2021-sustainable-modular-debiasing-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Anne Lauscher, Tobias Lueken, Goran Glavaš*2021 · 165 citations*
15. **[UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning](https://md.research.elamur.ai/free/en/papers/mao-2022-unipelt-unified-framework-parameter-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuning Mao, Lambert Mathias, Rui Hou, et al.*2022 · 162 citations*
16. **[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*
17. **[AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning](https://md.research.elamur.ai/free/en/papers/wang-2022-adamix-mixture-adaptations-parameter-efficient/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee, et al.*2022 · 132 citations*
18. **[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*
19. **[A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems](https://md.research.elamur.ai/free/en/papers/yi-2024-survey-recent-advances-llm-based/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Zihao Yi, Jiarui Ouyang, Zhe Xu, et al.*2024 · 129 citations*
20. **[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 · 129 citations*
21. [**Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning arXiv:2303.15647**Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/lialin-2023-scaling-down-scale-up-guide/)
22. **[Monolingual Adapters for Zero-Shot Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/philip-2020-monolingual-adapters-zero-shot-neural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jerin Philip, Alexandre Berard, Matthias Gallé, et al.*2020 · 114 citations*
23. **[RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs](https://md.research.elamur.ai/free/en/papers/akyurek-2023-rl4f-generating-natural-language-feedback/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Afra Feyza Akyurek, Ekin Akyurek, Ashwin Kalyan, et al.*2023 · 87 citations*
24. **[How to Adapt Your Pretrained Multilingual Model to 1600 Languages](https://md.research.elamur.ai/free/en/papers/ebrahimi-2021-how-adapt-your-pretrained-multilingual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Abteen Ebrahimi, Katharina Kann*2021 · 86 citations*
25. **[Bridging Argument Quality and Deliberative Quality Annotations with Adapters](https://md.research.elamur.ai/free/en/papers/falk-2023-bridging-argument-quality-deliberative-quality/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Neele Falk, Gabriella Lapesa*2023 · 21 citations*
26. [**DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules**Yanchen Liu, William Held, Diyi Yang*2023 · 20 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-dada-dialect-adaptation-dynamic-aggregation/)
27. [**Applying Occam's Razor to Transformer-Based Dependency Parsing: What Works, What Doesn't, and What is Really Necessary**Stefan Grünewald, Annemarie Friedrich, Jonas Kuhn*2021 · 18 citations*](https://md.research.elamur.ai/free/en/papers/grunewald-2021-applying-occam-s-razor-transformer/)
28. [**The eBible Corpus: Data and Model Benchmarks for Bible Translation for Low-Resource Languages**Vesa Akerman, David Baines, Damien Daspit, et al.*2023 · 9 citations*](https://md.research.elamur.ai/free/en/papers/akerman-2023-ebible-corpus-data-model-benchmarks/)
29. [**Pragmatic and Logical Inferences in NLI Systems: The Case of Conjunction Buttressing**Paolo Pedinotti, Emmanuele Chersoni, Enrico Santus, et al.*2022 · 2 citations*](https://md.research.elamur.ai/free/en/papers/pedinotti-2022-pragmatic-logical-inferences-nli-systems/)
30. **[NRD: A Hybrid Disentanglement Framework for Mitigating Interference in Multilingual Machine Translation](https://md.research.elamur.ai/free/en/papers/zhang-2026-nrd-hybrid-disentanglement-framework-mitigating/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Jiarui Zhang, Yifan Deng*2026*

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

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