--- title: "Who cites Karen Hambardzumyan" description: "145 papers of the Elamur Research library cite the works of Karen Hambardzumyan, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/karen-hambardzumyan/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/karen-hambardzumyan/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Karen Hambardzumyan](https://md.research.elamur.ai/free/en/authors/karen-hambardzumyan/) / cited by # Who cites Karen Hambardzumyan 145 citations of their works from 145 papers of our library (of the 140,884 whose bibliographies we read) · 22 with a page here, the most cited first · [← Karen Hambardzumyan](https://md.research.elamur.ai/free/en/authors/karen-hambardzumyan/) 1. [**LoRA: Low-Rank Adaptation of Large Language Models**Edward J. Hu, Yelong Shen, Phillip Wallis, et al.*2021 · 5,972 citations*](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/) 2. [**Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,375 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) 3. [**On the Opportunities and Risks of Foundation Models**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,044 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/) 4. [**Pre-trained Models for Natural Language Processing: A Survey**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,740 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/) 5. [**Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,548 citations*](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/) 6. [**PPT: Pre-trained Prompt Tuning for Few-shot Learning**Yuxian Gu, Xu Han, Zhiyuan Liu, et al.*2022 · 467 citations*](https://md.research.elamur.ai/free/en/papers/gu-2022-ppt-pre-trained-prompt-tuning/) 7. [**The Falcon Series of Open Language Models**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 436 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/) 8. [**Automatic Prompt Optimization with “Gradient Descent” and Beam Search**Reid Pryzant, Dan Iter, Jerry Li, et al.*2023 · 397 citations*](https://md.research.elamur.ai/free/en/papers/pryzant-2023-automatic-prompt-optimization-gradient-descent/) 9. [**Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification**Shengding Hu, Ning Ding, Huadong Wang, et al.*2022 · 360 citations*](https://md.research.elamur.ai/free/en/papers/hu-2022-knowledgeable-prompt-tuning-incorporating-knowledge/) 10. [**RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning**Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, et al.*2022 · 327 citations*](https://md.research.elamur.ai/free/en/papers/deng-2022-rlprompt-optimizing-discrete-text-prompts/) 11. [**CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies**Daniel Zeman, Jan Hajič, Martin Popel, et al.*2018 · 316 citations*](https://md.research.elamur.ai/free/en/papers/zeman-2018-conll-2018-shared-task-multilingual/) 12. [**Faith and Fate: Limits of Transformers on Compositionality**Nouha Dziri, Ximing Lu, Melanie Sclar, et al.*2023 · 313 citations*](https://md.research.elamur.ai/free/en/papers/dziri-2023-faith-fate-limits-transformers-compositionality/) 13. [**SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer**Tu Vu, Brian Lester, Noah Constant, et al.*2022 · 300 citations*](https://md.research.elamur.ai/free/en/papers/vu-2022-spot-better-frozen-model-adaptation/) 14. [**Surface Form Competition: Why the Highest Probability Answer Isn't Always Right**Ari Holtzman, Peter West, Vered Shwartz, et al.*2021 · 271 citations*](https://md.research.elamur.ai/free/en/papers/holtzman-2021-surface-form-competition-why-highest/) 15. [**MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies**Shengding Hu, Yuge Tu, Xu Han, et al.*2024 · 258 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-minicpm-unveiling-potential-small-language/) 16. [**Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity**Terry Yue Zhuo, Yujin Huang, Chunyang Chen, et al.*2023 · 243 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2023-red-teaming-chatgpt-jailbreaking-bias/) 17. [**Chameleon: Mixed-Modal Early-Fusion Foundation Models**Chameleon Team*2024 · 117 citations*](https://md.research.elamur.ai/free/en/papers/team-2024-chameleon-mixed-modal-early-fusion/) 18. [**SemDeDup: Data-efficient learning at web-scale through semantic deduplication**Amro Abbas, Kushal Tirumala, Dániel Simig, et al.*2023 · 94 citations*](https://md.research.elamur.ai/free/en/papers/abbas-2023-semdedup-data-efficient-learning-web/) 19. [**Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies**Chaofan Tao, Qian Liu, Longxu Dou, et al.*2024 · 42 citations*](https://md.research.elamur.ai/free/en/papers/tao-2024-scaling-laws-vocabulary-larger-models/) 20. [**Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations**Alexander Hägele, Elie Bakouch, Atli Kosson, et al.*2024 · 24 citations*](https://md.research.elamur.ai/free/en/papers/hagele-2024-scaling-laws-compute-optimal-training/) 21. [**Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognition**Haodong Zhao, Ruifang He, Mengnan Xiao, et al.*2023 · 19 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-infusing-hierarchical-guidance-into-prompt/) 22. [**Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection**Erik Arakelyan, Arnav Arora, Isabelle Augenstein*2023 · 19 citations*](https://md.research.elamur.ai/free/en/papers/arakelyan-2023-topic-guided-sampling-data-efficient/) 123 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/karen-hambardzumyan/) — Karen Hambardzumyan