---
title: "Who cites Hierarchical Neural Story — 794 citing papers"
description: "794 papers of the Elamur Research library cite «Hierarchical Neural Story Generation», found in their bibliographies · page 1 of 2 · 1–100; the…"
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---
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# Who cites «Hierarchical Neural Story Generation»

794 papers of our library cite it (of the 149,439 whose bibliographies we read) · 142 with a page here, the most cited first · page 1 of 2 · 1–100 · [← back to «Hierarchical Neural Story Generation»](https://md.research.elamur.ai/free/en/papers/fan-2018-hierarchical-neural-story-generation/)

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1. [**Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks arXiv:2005.11401**Patrick Lewis, Ethan Perez, Aleksandra Piktus, et al.*2020 · 6,256 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-retrieval-augmented-generation-knowledge-intensive/)
2. **[fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://md.research.elamur.ai/free/en/papers/ott-2019-fairseq-fast-extensible-toolkit-sequence/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Myle Ott, Sergey Edunov, Alexei Baevski, et al.*2019 · 3,984 citations*
3. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
4. [**Self-Consistency Improves Chain of Thought Reasoning in Language Models arXiv:2203.11171**Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.*2022 · 3,121 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/)
5. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
6. [**The Curious Case of Neural Text Degeneration arXiv:1904.09751**Ari Holtzman, Jan Buys, Li Du, et al.*2019 · 2,660 citations*](https://md.research.elamur.ai/free/en/papers/holtzman-2019-curious-case-neural-text-degeneration/)
7. **[Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://md.research.elamur.ai/free/en/papers/narayan-2018-don-t-give-me-details/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shashi Narayan, Shay B. Cohen, Mirella Lapata*2018 · 2,149 citations*
8. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
9. **[Competition-Level Code Generation with AlphaCode](https://md.research.elamur.ai/free/en/papers/li-2022-competition-level-code-generation-alphacode/) [Science](https://md.research.elamur.ai/free/en/venues/science/)**Yujia Li, David Choi, Junyoung Chung, et al.*2022 · 1,564 citations*
10. [**CTRL: A Conditional Transformer Language Model for Controllable Generation arXiv:1909.05858**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,291 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/)
11. **[Recipes for Building an Open-Domain Chatbot](https://md.research.elamur.ai/free/en/papers/roller-2021-recipes-building-open-domain-chatbot/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Stephen Roller, Emily Dinan, Naman Goyal, et al.*2021 · 1,214 citations*
12. **[Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://md.research.elamur.ai/free/en/papers/reynolds-2021-prompt-programming-large-language-models/) [Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA)](https://md.research.elamur.ai/free/en/venues/extended-abstracts-of-the-chi-conference-on-human-factors-in-computing-systems/)**Laria Reynolds, Kyle McDonell*2021 · 1,121 citations*
13. [**Extracting Training Data from Large Language Models arXiv:2012.07805**Nicholas Carlini, Florian Tramer, Eric Wallace, et al.*2020 · 955 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2020-extracting-training-data-large-language/)
14. **[GLTR: Statistical Detection and Visualization of Generated Text](https://md.research.elamur.ai/free/en/papers/gehrmann-2019-gltr-statistical-detection-visualization-generated/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sebastian Gehrmann, Hendrik Strobelt, Alexander M. Rush*2019 · 697 citations*
15. [**Towards a Human-like Open-Domain Chatbot arXiv:2001.09977**Daniel Adiwardana, Minh-Thang Luong, David R. So, et al.*2020 · 592 citations*](https://md.research.elamur.ai/free/en/papers/adiwardana-2020-towards-human-like-open-domain/)
16. **[ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://md.research.elamur.ai/free/en/papers/hartvigsen-2022-toxigen-large-scale-machine-generated/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, et al.*2022 · 578 citations*
17. [**Plug and Play Language Models: A Simple Approach to Controlled Text Generation arXiv:1912.02164**Sumanth Dathathri, Andrea Madotto, Janice Lan, et al.*2019 · 578 citations*](https://md.research.elamur.ai/free/en/papers/dathathri-2019-plug-play-language-models-simple/)
18. **[Document Ranking with a Pretrained Sequence-to-Sequence Model](https://md.research.elamur.ai/free/en/papers/nogueira-2020-document-ranking-pretrained-sequence-sequence/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, et al.*2020 · 543 citations*
19. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/)
20. [**Neural Text Generation with Unlikelihood Training arXiv:1908.04319**Sean Welleck, Ilia Kulikov, Stephen Roller, et al.*2019 · 513 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/)
21. [**DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature arXiv:2301.11305**Eric Mitchell, Yoonho Lee, Alexander Khazatsky, et al.*2023 · 481 citations*](https://md.research.elamur.ai/free/en/papers/mitchell-2023-detectgpt-zero-shot-machine-generated/)
22. [**Defending Against Neural Fake News arXiv:1905.12616**Rowan Zellers, Ari Holtzman, Hannah Rashkin, et al.*2019 · 478 citations*](https://md.research.elamur.ai/free/en/papers/zellers-2019-defending-against-neural-fake-news/)
23. **[A Transformer-based Approach for Source Code Summarization](https://md.research.elamur.ai/free/en/papers/ahmad-2020-transformer-based-approach-source-code/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, et al.*2020 · 431 citations*
24. **[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*
25. **[DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts](https://md.research.elamur.ai/free/en/papers/liu-2021-dexperts-decoding-time-controlled-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alisa Liu, Maarten Sap, Ximing Lu, et al.*2021 · 409 citations*
26. [**Retrieval Augmentation Reduces Hallucination in Conversation arXiv:2104.07567**Kurt Shuster, Spencer Poff, Moya Chen, et al.*2021 · 392 citations*](https://md.research.elamur.ai/free/en/papers/shuster-2021-retrieval-augmentation-reduces-hallucination-conversation/)
27. [**Document Expansion by Query Prediction arXiv:1904.08375**Rodrigo Nogueira, Wei Yang, Jimmy Lin, et al.*2019 · 320 citations*](https://md.research.elamur.ai/free/en/papers/nogueira-2019-document-expansion-query-prediction/)
28. [**Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads arXiv:2401.10774**Tianle Cai, Yuhong Li, Zhengyang Geng, et al.*2024 · 278 citations*](https://md.research.elamur.ai/free/en/papers/cai-2024-medusa-simple-llm-inference-acceleration/)
29. **[Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation](https://md.research.elamur.ai/free/en/papers/dinan-2020-queens-are-powerful-too-mitigating/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Emily Dinan, Angela Fan, Adina Williams, et al.*2020 · 270 citations*
30. **[On Extractive and Abstractive Neural Document Summarization with Transformer Language Models](https://md.research.elamur.ai/free/en/papers/pilault-2020-extractive-abstractive-neural-document-summarization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jonathan Pilault, Raymond Li, Sandeep Subramanian, et al.*2020 · 261 citations*
31. **[DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Boxin Wang, Weixin Chen, Hengzhi Pei, et al.*2023 · 259 citations*
32. **[Societal Biases in Language Generation: Progress and Challenges](https://md.research.elamur.ai/free/en/papers/sheng-2021-societal-biases-language-generation-progress/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Emily Sheng, Kai-Wei Chang, Prem Natarajan, et al.*2021 · 257 citations*
33. [**BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model arXiv:1902.04094**Alex Wang, Kyunghyun Cho*2019 · 256 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-bert-has-mouth-must-speak/)
34. **[Generating Datasets with Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/schick-2021-generating-datasets-pretrained-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Timo Schick, Hinrich Schutze*2021 · 254 citations*
35. [**Contrastive Decoding: Open-ended Text Generation as Optimization arXiv:2210.15097**Xiang Lisa Li, Ari Holtzman, Daniel Fried, et al.*2022 · 253 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-contrastive-decoding-open-ended-text/)
36. [**RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment arXiv:2304.06767**Hanze Dong, Wei Xiong, Deepanshu Goyal, et al.*2023 · 252 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-raft-reward-ranked-finetuning-generative/)
37. [**MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers arXiv:2102.01454**Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, et al.*2021 · 251 citations*](https://md.research.elamur.ai/free/en/papers/pillutla-2021-mauve-measuring-gap-between-neural/)
38. **[Automatic Detection of Machine Generated Text: A Critical Survey](https://md.research.elamur.ai/free/en/papers/jawahar-2020-automatic-detection-machine-generated-text/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Ganesh Jawahar, Muhammad Abdul-Mageed, Laks Lakshmanan V.S.*2020 · 247 citations*
39. **[MedDialog: Large-scale Medical Dialogue Datasets](https://md.research.elamur.ai/free/en/papers/zeng-2020-meddialog-large-scale-medical-dialogue/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Guangtao Zeng, Wenmian Yang, Zeqian Ju, et al.*2020 · 247 citations*
40. **[ZeroGen: Efficient Zero-shot Learning via Dataset Generation](https://md.research.elamur.ai/free/en/papers/ye-2022-zerogen-efficient-zero-shot-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiacheng Ye, Jiahui Gao, Qintong Li, et al.*2022 · 245 citations*
41. **[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*
42. [**Recursively Summarizing Books with Human Feedback arXiv:2109.10862**Jeff Wu, Long Ouyang, Daniel M. Ziegler, et al.*2021 · 208 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-recursively-summarizing-books-human-feedback/)
43. **[Q²: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering](https://md.research.elamur.ai/free/en/papers/honovich-2021-q-2-evaluating-factual-consistency/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Or Honovich, Leshem Choshen, Roee Aharoni, et al.*2021 · 196 citations*
44. [**LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods arXiv:2412.05579**Haitao Li, Qian Dong, Junjie Chen, et al.*2024 · 195 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llms-judges-comprehensive-survey-llm/)
45. [**The Prompt Report: A Systematic Survey of Prompt Engineering Techniques arXiv:2406.06608**Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.*2024 · 189 citations*](https://md.research.elamur.ai/free/en/papers/schulhoff-2024-prompt-report-systematic-survey-prompt/)
46. **[Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 182 citations*
47. **[DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://md.research.elamur.ai/free/en/papers/su-2023-detectllm-leveraging-log-rank-information/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinyan Su, Terry Zhuo, Di Wang, et al.*2023 · 172 citations*
48. [**Deduplicating Training Data Mitigates Privacy Risks in Language Models arXiv:2202.06539**Nikhil Kandpal, Eric Wallace, Colin Raffel*2022 · 172 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-deduplicating-training-data-mitigates-privacy/)
49. **[Quark: Controllable Text Generation with Reinforced Unlearning](https://md.research.elamur.ai/free/en/papers/lu-2022-quark-controllable-text-generation-reinforced/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ximing Lu, Sean Welleck, Jack Hessel, et al.*2022 · 170 citations*
50. [**Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation arXiv:2310.06987**Yangsibo Huang, Samyak Gupta, Mengzhou Xia, et al.*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-catastrophic-jailbreak-open-source-llms/)
51. **[Neural Syntactic Preordering for Controlled Paraphrase Generation](https://md.research.elamur.ai/free/en/papers/goyal-2020-neural-syntactic-preordering-controlled-paraphrase/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tanya Goyal, Greg Durrett*2020 · 167 citations*
52. **[Membership Inference Attacks against Language Models via Neighbourhood Comparison](https://md.research.elamur.ai/free/en/papers/mattern-2023-membership-inference-attacks-against-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, et al.*2023 · 165 citations*
53. **[If beam search is the answer, what was the question?](https://md.research.elamur.ai/free/en/papers/meister-2020-if-beam-search-is-answer/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Clara Meister, Ryan Cotterell, Tim Vieira*2020 · 163 citations*
54. [**Reasoning with Language Model Prompting: A Survey arXiv:2212.09597**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 161 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/)
55. **[MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models](https://md.research.elamur.ai/free/en/papers/xu-2020-megatron-cntrl-controllable-story-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Peng Xu, Mostofa Patwary, Mohammad Shoeybi, et al.*2020 · 158 citations*
56. **[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*
57. **[Explaining NLP Models via Minimal Contrastive Editing (MiCE)](https://md.research.elamur.ai/free/en/papers/ross-2021-explaining-nlp-models-minimal-contrastive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Alexis Ross, Ana Marasović, Matthew Peters*2021 · 155 citations*
58. **[Self-Evaluation Guided Beam Search for Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2023-self-evaluation-guided-beam-search/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.*2023 · 149 citations*
59. [**Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature arXiv:2310.05130**Guangsheng Bao, Yanbin Zhao, Zhiyang Teng, et al.*2023 · 148 citations*](https://md.research.elamur.ai/free/en/papers/bao-2023-fast-detectgpt-efficient-zero-shot/)
60. **[Quality-Aware Decoding for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/fernandes-2022-quality-aware-decoding-neural-machine/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Patrick Fernandes, António Farinhas, Ricardo Rei, et al.*2022 · 146 citations*
61. [**GLUCOSE: GeneraLized and COntextualized Story Explanations**Nasrin Mostafazadeh, Aditya Kalyanpur, Lori Moon, et al.*2020 · 145 citations*](https://md.research.elamur.ai/free/en/papers/mostafazadeh-2020-glucose-generalized-contextualized-story-explanations/)
62. **[NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics](https://md.research.elamur.ai/free/en/papers/lu-2022-neurologic-esque-decoding-constrained-text/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ximing Lu, Sean Welleck, Peter West, et al.*2022 · 140 citations*
63. **[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*
64. **[Improved Natural Language Generation via Loss Truncation](https://md.research.elamur.ai/free/en/papers/kang-2020-improved-natural-language-generation-loss/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Daniel Kang, Tatsunori B. Hashimoto*2020 · 130 citations*
65. **[MAGE: Machine-generated Text Detection in the Wild](https://md.research.elamur.ai/free/en/papers/li-2024-mage-machine-generated-text-detection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yafu Li, Qintong Li, Leyang Cui, et al.*2024 · 129 citations*
66. **[SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding](https://md.research.elamur.ai/free/en/papers/xu-2024-safedecoding-defending-against-jailbreak-attacks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhangchen Xu, Fengqing Jiang, Luyao Niu, et al.*2024 · 127 citations*
67. [**A Recipe For Arbitrary Text Style Transfer with Large Language Models arXiv:2109.03910**Emily Reif, Daphne Ippolito, Ann Yuan, et al.*2021 · 127 citations*](https://md.research.elamur.ai/free/en/papers/reif-2021-recipe-arbitrary-text-style-transfer/)
68. [**Pretraining Language Models with Human Preferences arXiv:2302.08582**Tomasz Korbak, Kejian Shi, Angelica Chen, et al.*2023 · 121 citations*](https://md.research.elamur.ai/free/en/papers/korbak-2023-pretraining-language-models-human-preferences/)
69. **[Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-models-are-not/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen, et al.*2023 · 119 citations*
70. **[Evaluating the Evaluation of Diversity in Natural Language Generation](https://md.research.elamur.ai/free/en/papers/tevet-2021-evaluating-evaluation-diversity-natural-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Guy Tevet, Jonathan Berant*2021 · 106 citations*
71. **[I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling](https://md.research.elamur.ai/free/en/papers/nie-2021-i-like-fish-especially-dolphins/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixin Nie, Mary Williamson, Mohit Bansal, et al.*2021 · 101 citations*
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