--- title: "Who cites Neural Text Generation with Unlikelihood Training" description: "271 papers of the Elamur Research library cite «Neural Text Generation with Unlikelihood Training», found in their bibliographies · page 1 of 2; the most…" canonical: "https://research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Neural Text Generation with Unlikelihood Training](https://md.research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/) / cited by # Who cites «Neural Text Generation with Unlikelihood Training» 271 papers of our library cite it (of the 140884 whose bibliographies we read) · 30 with a page here, the most cited first · page 1 of 2 · [← the paper](https://md.research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/) **1** [2](https://md.research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/cited-by/page/2/) - [Survey of Hallucination in Natural Language Generation](https://md.research.elamur.ai/free/en/papers/ji-2022-survey-hallucination-natural-language-generation/) — Ziwei Ji, Nayeon Lee, Rita Frieske, et al. · 2022 · 5 947 citations - [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://md.research.elamur.ai/free/en/papers/rafailov-2023-direct-preference-optimization-language-model/) — Rafael Rafailov, Archit Sharma, Eric Mitchell, et al. · 2023 · 2 478 citations - [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/zhang-2019-pegasus-pre-training-extracted-gap/) — Jingqing Zhang, Yao Zhao, Mohammad Saleh, et al. · 2019 · 1 806 citations - [OPT: Open Pre-trained Transformer Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/) — Susan Zhang, Stephen Roller, Naman Goyal, et al. · 2022 · 1 709 citations - [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/) — Samuel Gehman, Suchin Gururangan, Maarten Sap, et al. · 2020 · 1 277 citations - [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/) — Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al. · 2019 · 1 273 citations - [Recipes for Building an Open-Domain Chatbot](https://md.research.elamur.ai/free/en/papers/roller-2021-recipes-building-open-domain-chatbot/) — Stephen Roller, Emily Dinan, Naman Goyal, et al. · 2021 · 1 206 citations - [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/) — Haokun Liu, Derek Tam, Mohammed Muqeeth, et al. · 2022 · 735 citations - [Red Teaming Language Models with Language Models](https://md.research.elamur.ai/free/en/papers/perez-2022-red-teaming-language-models-language/) — Ethan Perez, Saffron Huang, Francis Song, et al. · 2022 · 665 citations - [Augmented Language Models: a Survey](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/) — Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al. · 2023 · 362 citations - [Symbolic Knowledge Distillation: from General Language Models to Commonsense Models](https://md.research.elamur.ai/free/en/papers/west-2022-symbolic-knowledge-distillation-general-language/) — Peter West, Chandra Bhagavatula, Jack Hessel, et al. · 2022 · 343 citations - [Challenges and Applications of Large Language Models](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/) — Jean Kaddour, Joshua Harris, Maximilian Mozes, et al. · 2023 · 300 citations - [CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/cao-2021-cliff-contrastive-learning-improving-faithfulness/) — Shuyang Cao, Lu Wang · 2021 · 257 citations - [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/) — Boxin Wang, Weixin Chen, Hengzhi Pei, et al. · 2023 · 246 citations - [Contrastive Decoding: Open-ended Text Generation as Optimization](https://md.research.elamur.ai/free/en/papers/li-2022-contrastive-decoding-open-ended-text/) — Xiang Lisa Li, Ari Holtzman, Daniel Fried, et al. · 2022 · 246 citations - [MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers](https://md.research.elamur.ai/free/en/papers/pillutla-2021-mauve-measuring-gap-between-neural/) — Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, et al. · 2021 · 243 citations - [Automatic Detection of Machine Generated Text: A Critical Survey](https://md.research.elamur.ai/free/en/papers/jawahar-2020-automatic-detection-machine-generated-text/) — Ganesh Jawahar, Muhammad Abdul-Mageed, Laks Lakshmanan V.S. · 2020 · 243 citations - [ZeroGen: Efficient Zero-shot Learning via Dataset Generation](https://md.research.elamur.ai/free/en/papers/ye-2022-zerogen-efficient-zero-shot-learning/) — Jiacheng Ye, Jiahui Gao, Qintong Li, et al. · 2022 · 242 citations - [Detecting Hallucinated Content in Conditional Neural Sequence Generation](https://md.research.elamur.ai/free/en/papers/zhou-2021-detecting-hallucinated-content-conditional-neural/) — Chunting Zhou, Graham Neubig, Jiatao Gu, et al. · 2021 · 229 citations - [Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training](https://md.research.elamur.ai/free/en/papers/li-2020-don-t-say-that-making/) — Margaret Li, Stephen Roller, Ilia Kulikov, et al. · 2020 · 214 citations - [Generating Training Data with Language Models: Towards Zero-Shot Language Understanding](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/) — Yu Meng, Jiaxin Huang, Yu Zhang, et al. · 2022 · 206 citations - [Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/zhang-2020-grounded-conversation-generation-as-guided/) — Houyu Zhang, Zhenghao Liu, Chenyan Xiong, et al. · 2020 · 181 citations - [Quark: Controllable Text Generation with Reinforced Unlearning](https://md.research.elamur.ai/free/en/papers/lu-2022-quark-controllable-text-generation-reinforced/) — Ximing Lu, Sean Welleck, Jack Hessel, et al. · 2022 · 164 citations - [Locally Typical Sampling](https://md.research.elamur.ai/free/en/papers/meister-2023-locally-typical-sampling/) — Clara Meister, Tiago Pimentel, Gian Wiher, et al. · 2023 · 152 citations - [Paragraph-level Simplification of Medical Texts](https://md.research.elamur.ai/free/en/papers/devaraj-2021-paragraph-level-simplification-medical-texts/) — Ashwin Devaraj, Iain Marshall, Byron Wallace, et al. · 2021 · 139 citations **1** [2](https://md.research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/cited-by/page/2/)