--- title: "Who cites Commonsense Knowledge Mining from Pretrained…" description: "156 papers of the Elamur Research library cite «Commonsense Knowledge Mining from Pretrained Models», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/davison-2019-commonsense-knowledge-mining-pretrained-models/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/davison-2019-commonsense-knowledge-mining-pretrained-models/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Commonsense Knowledge Mining from Pretrained…](https://md.research.elamur.ai/free/en/papers/davison-2019-commonsense-knowledge-mining-pretrained-models/) / cited by # Who cites «Commonsense Knowledge Mining from Pretrained Models» 156 papers of our library cite it (of the 140,884 whose bibliographies we read) · 21 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/davison-2019-commonsense-knowledge-mining-pretrained-models/) 1. [**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/) 2. [**Making Pre-trained Language Models Better Few-shot Learners**Tianyu Gao, Adam Fisch, Danqi Chen*2021 · 1,952 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-making-pre-trained-language-models/) 3. [**Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference**Timo Schick, Hinrich Schütze*2021 · 1,796 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-exploiting-cloze-questions-few-shot/) 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. [**Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity**Yao Lu, Max Bartolo, Alastair Moore, et al.*2022 · 1,187 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-fantastically-ordered-prompts-where-find/) 7. [**It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners**Timo Schick, Hinrich Schütze*2021 · 1,118 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-it-s-not-just-size/) 8. [**Large Language Models Are Human-Level Prompt Engineers**Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, et al.*2022 · 677 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2022-large-language-models-human-level/) 9. [**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/) 10. [**Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents**Wenlong Huang, Pieter Abbeel, Deepak Pathak, et al.*2022 · 420 citations*](https://md.research.elamur.ai/free/en/papers/huang-2022-language-models-zero-shot-planners/) 11. [**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/) 12. [**Symbolic Knowledge Distillation: from General Language Models to Commonsense Models**Peter West, Chandra Bhagavatula, Jack Hessel, et al.*2022 · 343 citations*](https://md.research.elamur.ai/free/en/papers/west-2022-symbolic-knowledge-distillation-general-language/) 13. [**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/) 14. [**Trusting Your Evidence: Hallucinate Less with Context-aware Decoding**Weijia Shi, Xiaochuang Han, Mike Lewis, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/shi-2024-trusting-your-evidence-hallucinate-less/) 15. [**COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs**Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, et al.*2020 · 235 citations*](https://md.research.elamur.ai/free/en/papers/hwang-2020-comet-atomic-2020-symbolic-neural/) 16. [**Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language Models**Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, et al.*2020 · 216 citations*](https://md.research.elamur.ai/free/en/papers/lin-2020-birds-have-four-legs-numersense/) 17. [**Generative Data Augmentation for Commonsense Reasoning**Yiben Yang, Chaitanya Malaviya, Jared Fernandez, et al.*2020 · 194 citations*](https://md.research.elamur.ai/free/en/papers/yang-2020-generative-data-augmentation-commonsense-reasoning/) 18. [**Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning**Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.*2022 · 162 citations*](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/) 19. [**Reasoning with Language Model Prompting: A Survey**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 160 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/) 20. [**Automatic Story Generation: Challenges and Attempts**Amal Alabdulkarim, Siyan Li, Xiangyu Peng*2021 · 61 citations*](https://md.research.elamur.ai/free/en/papers/alabdulkarim-2021-automatic-story-generation-challenges-attempts/) 21. [**Building Common Ground in Dialogue: A Survey**Tatiana Anikina, Alina Leippert, Simon Ostermann*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/anikina-2025-building-common-ground-dialogue-survey/) 135 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/davison-2019-commonsense-knowledge-mining-pretrained-models/) — Commonsense Knowledge Mining from Pretrained Models