--- title: "Who cites Do Massively Pretrained — 79 citing papers" description: "79 papers of the Elamur Research library cite «Do Massively Pretrained Language Models Make Better Storytellers?», found in their…" canonical: "https://research.elamur.ai/free/en/papers/see-2019-massively-pretrained-language-models-make/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/see-2019-massively-pretrained-language-models-make/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Do Massively Pretrained Language Models Make…](https://md.research.elamur.ai/free/en/papers/see-2019-massively-pretrained-language-models-make/) / cited by # Who cites «Do Massively Pretrained Language Models Make Better Storytellers?» 79 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «Do Massively Pretrained Language Models Make Better Storytellers?»](https://md.research.elamur.ai/free/en/papers/see-2019-massively-pretrained-language-models-make/) 1. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/) 2. **[On Faithfulness and Factuality in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/maynez-2020-faithfulness-factuality-abstractive-summarization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joshua Maynez, Shashi Narayan, Bernd Bohnet, et al.*2020 · 1,857 citations* 3. **[The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhiheng Xi, Wenxiang Chen, Xin Guo, et al.*2023 · 1,272 citations* 4. **[Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://md.research.elamur.ai/free/en/papers/ippolito-2019-automatic-detection-generated-text-easiest/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, et al.*2019 · 411 citations* 5. **[Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://md.research.elamur.ai/free/en/papers/krishna-2023-paraphrasing-evades-detectors-ai-generated/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kalpesh Krishna, Yixiao Song, Marzena Karpinska, et al.*2023 · 310 citations* 6. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/) 7. **[Enabling Language Models to Fill in the Blanks](https://md.research.elamur.ai/free/en/papers/donahue-2020-enabling-language-models-fill-blanks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chris Donahue, Mina Lee, Percy Liang*2020 · 209 citations* 8. **[Locally Typical Sampling](https://md.research.elamur.ai/free/en/papers/meister-2023-locally-typical-sampling/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Clara Meister, Tiago Pimentel, Gian Wiher, et al.*2023 · 157 citations* 9. **[A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://md.research.elamur.ai/free/en/papers/liu-2022-token-level-reference-free-hallucination/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Liu, Yizhe Zhang, Chris Brockett, et al.*2022 · 142 citations* 10. **[TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation](https://md.research.elamur.ai/free/en/papers/uchendu-2021-turingbench-benchmark-environment-turing-test/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Adaku Uchendu, Zeyu Ma, Thai Le, et al.*2021 · 140 citations* 11. **[Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions](https://md.research.elamur.ai/free/en/papers/aliannejadi-2021-building-evaluating-open-domain-dialogue/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mohammad Aliannejadi, Julia Kiseleva, Aleksandr Chuklin, et al.*2021 · 111 citations* 12. [**Does Writing with Language Models Reduce Content Diversity? arXiv:2309.05196**Vishakh Padmakumar, He He*2023 · 82 citations*](https://md.research.elamur.ai/free/en/papers/padmakumar-2023-writing-language-models-reduce-content/) 13. **[Assessing Discourse Relations in Language Generation from GPT-2](https://md.research.elamur.ai/free/en/papers/ko-2020-assessing-discourse-relations-language-generation/) [International Natural Language Generation Conference (INLG)](https://md.research.elamur.ai/free/en/venues/inlg/)**Wei-Jen Ko, Junyi Jessy Li*2020 · 30 citations* 14. **[Suri: Multi-constraint Instruction Following in Long-form Text Generation](https://md.research.elamur.ai/free/en/papers/pham-2024-suri-multi-constraint-instruction-following/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chau Minh Pham, Simeng Sun, Mohit Iyyer*2024 · 19 citations* 65 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/see-2019-massively-pretrained-language-models-make/) — Do Massively Pretrained Language Models Make Better Storytellers?