---
title: "Who cites Lav R. Varshney — 613 papers of our library"
description: "613 papers of the Elamur Research library cite the works of Lav R. Varshney, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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---
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# Who cites Lav R. Varshney

620 citations of their works from 613 papers of our library (of the 149,439 whose bibliographies we read) · 131 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Lav R. Varshney](https://md.research.elamur.ai/free/en/authors/lav-r-varshney/)

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1. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,325 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
2. [**Training language models to follow instructions with human feedback arXiv:2203.02155**Long Ouyang, Jeff Wu, Xu Jiang, et al.*2022 · 10,224 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
3. **[Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,423 citations*
4. [**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/)
5. [**Evaluating Large Language Models Trained on Code arXiv:2107.03374**Mark Chen, Jerry Tworek, Heewoo Jun, et al.*2021 · 2,659 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-evaluating-large-language-models-trained/)
6. **[Toolformer: Language Models Can Teach Themselves to Use Tools](https://md.research.elamur.ai/free/en/papers/schick-2023-toolformer-language-models-can-teach/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, et al.*2023 · 2,065 citations*
7. [**Pre-trained Models for Natural Language Processing: A Survey arXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,759 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
8. **[Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data](https://md.research.elamur.ai/free/en/papers/bender-2020-climbing-nlu-meaning-form-understanding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Emily M. Bender, Alexander Koller*2020 · 1,382 citations*
9. **[RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Samuel Gehman, Suchin Gururangan, Maarten Sap, et al.*2020 · 1,306 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. **[Social Bias Frames: Reasoning about Social and Power Implications of Language](https://md.research.elamur.ai/free/en/papers/sap-2020-social-bias-frames-reasoning-about/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Maarten Sap, Saadia Gabriel, Lianhui Qin, et al.*2020 · 684 citations*
14. [**Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection arXiv:2310.11511**Akari Asai, Zeqiu Wu, Yizhong Wang, et al.*2023 · 677 citations*](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/)
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. [**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/)
17. [**Release Strategies and the Social Impacts of Language Models arXiv:1908.09203**Irene Solaiman, Miles Brundage, Jack Clark, et al.*2019 · 554 citations*](https://md.research.elamur.ai/free/en/papers/solaiman-2019-release-strategies-social-impacts-language/)
18. [**Rethinking Attention with Performers arXiv:2009.14794**Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, et al.*2020 · 491 citations*](https://md.research.elamur.ai/free/en/papers/choromanski-2020-rethinking-attention-performers/)
19. [**Data Augmentation using Pre-trained Transformer Models arXiv:2003.02245**Varun Kumar, Ashutosh Choudhary, Eunah Cho*2020 · 474 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2020-data-augmentation-using-pre-trained/)
20. **[AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://md.research.elamur.ai/free/en/papers/dubois-2023-alpacafarm-simulation-framework-methods-that/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yann Dubois, Xuechen Li, Rohan Taori, et al.*2023 · 456 citations*
21. **[GeDi: Generative Discriminator Guided Sequence Generation](https://md.research.elamur.ai/free/en/papers/krause-2021-gedi-generative-discriminator-guided-sequence/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, et al.*2021 · 449 citations*
22. [**Diffusion-LM Improves Controllable Text Generation arXiv:2205.14217**Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, et al.*2022 · 440 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-diffusion-lm-improves-controllable-text/)
23. **[Experience Grounds Language](https://md.research.elamur.ai/free/en/papers/bisk-2020-experience-grounds-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yonatan Bisk, Ari Holtzman, Jesse Thomason, et al.*2020 · 439 citations*
24. **[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*
25. **[Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering](https://md.research.elamur.ai/free/en/papers/siriwardhana-2023-improving-domain-adaptation-retrieval-augmented/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, et al.*2023 · 403 citations*
26. [**A Simple Language Model for Task-Oriented Dialogue arXiv:2005.00796**Ehsan Hosseini-Asl, Bryan McCann, Chien-Sheng Wu, et al.*2020 · 399 citations*](https://md.research.elamur.ai/free/en/papers/hosseini-asl-2020-simple-language-model-task-oriented/)
27. **[TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue](https://md.research.elamur.ai/free/en/papers/wu-2020-tod-bert-pre-trained-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chien-Sheng Wu, Steven C.H. Hoi, Richard Socher, et al.*2020 · 361 citations*
28. [**Reformulating Unsupervised Style Transfer as Paraphrase Generation**Kalpesh Krishna, John Wieting, Mohit Iyyer*2020 · 350 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2020-reformulating-unsupervised-style-transfer-as/)
29. **[RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/deng-2022-rlprompt-optimizing-discrete-text-prompts/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, et al.*2022 · 338 citations*
30. [**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/)
31. **[Text Classification via Large Language Models](https://md.research.elamur.ai/free/en/papers/sun-2023-text-classification-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaofei Sun, Xiaoya Li, Jiwei Li, et al.*2023 · 292 citations*
32. **[Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://md.research.elamur.ai/free/en/papers/smith-2020-can-you-put-it-all/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Eric Michael Smith, Mary Williamson, Kurt Shuster, et al.*2020 · 289 citations*
33. **[Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models](https://md.research.elamur.ai/free/en/papers/wu-2021-polyjuice-generating-counterfactuals-explaining-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, et al.*2021 · 277 citations*
34. [**DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models arXiv:2309.03883**Yung-Sung Chuang, Yujia Xie, Hongyin Luo, et al.*2023 · 269 citations*](https://md.research.elamur.ai/free/en/papers/chuang-2023-dola-decoding-contrasting-layers-improves/)
35. **[TLDR: Extreme Summarization of Scientific Documents](https://md.research.elamur.ai/free/en/papers/cachola-2020-tldr-extreme-summarization-scientific-documents/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Isabel Cachola, Kyle Lo, Arman Cohan, et al.*2020 · 262 citations*
36. [**The Curse of Recursion: Training on Generated Data Makes Models Forget arXiv:2305.17493**Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, et al.*2023 · 255 citations*](https://md.research.elamur.ai/free/en/papers/shumailov-2023-curse-recursion-training-generated-data/)
37. **[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*
38. **[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*
39. **[FUDGE: Controlled Text Generation With Future Discriminators](https://md.research.elamur.ai/free/en/papers/yang-2021-fudge-controlled-text-generation-future/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kevin Yang, Dan Klein*2021 · 239 citations*
40. **[TweepFake: about Detecting Deepfake Tweets](https://md.research.elamur.ai/free/en/papers/fagni-2020-tweepfake-about-detecting-deepfake-tweets/) [PLoS ONE](https://md.research.elamur.ai/free/en/venues/plos-one/)**Tiziano Fagni, Fabrizio Falchi, Margherita Gambini, et al.*2020 · 234 citations*
41. [**Teaching language models to support answers with verified quotes arXiv:2203.11147**Jacob Menick, Maja Trebacz, Vladimir Mikulik, et al.*2022 · 218 citations*](https://md.research.elamur.ai/free/en/papers/menick-2022-teaching-language-models-support-answers/)
42. [**Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP arXiv:2103.00453**Timo Schick, Sahana Udupa, Hinrich Schutze*2021 · 213 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-self-diagnosis-self-debiasing-proposal/)
43. [**Generating Training Data with Language Models: Towards Zero-Shot Language Understanding arXiv:2202.04538**Yu Meng, Jiaxin Huang, Yu Zhang, et al.*2022 · 210 citations*](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/)
44. **[On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?](https://md.research.elamur.ai/free/en/papers/dziri-2022-origin-hallucinations-conversational-models-is/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nouha Dziri, Sivan Milton, Mo Yu, et al.*2022 · 208 citations*
45. **[Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space](https://md.research.elamur.ai/free/en/papers/li-2020-optimus-organizing-sentences-pre-trained/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chunyuan Li, Xiang Gao, Yuan Li, et al.*2020 · 208 citations*
46. **[Training Question Answering Models From Synthetic Data](https://md.research.elamur.ai/free/en/papers/puri-2020-training-question-answering-models-synthetic/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Raul Puri, Ryan Spring, Mohammad Shoeybi, et al.*2020 · 208 citations*
47. **[DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks](https://md.research.elamur.ai/free/en/papers/ding-2020-daga-data-augmentation-generation-approach/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bosheng Ding, Linlin Liu, Lidong Bing, et al.*2020 · 203 citations*
48. [**Recipes for Safety in Open-domain Chatbots arXiv:2010.07079**Jing Xu, Da Ju, Margaret Li, et al.*2020 · 201 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-recipes-safety-open-domain-chatbots/)
49. **[Authorship Attribution for Neural Text Generation](https://md.research.elamur.ai/free/en/papers/uchendu-2020-authorship-attribution-neural-text-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Adaku Uchendu, Thai Le, Kai Shu, et al.*2020 · 196 citations*
50. [**Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP arXiv:2112.10508**Sabrina J. Mielke, Zaid Alyafeai, Elizabeth Salesky, et al.*2021 · 191 citations*](https://md.research.elamur.ai/free/en/papers/mielke-2021-between-words-characters-brief-history/)
51. [**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/)
52. [**8-bit Optimizers via Block-wise Quantization arXiv:2110.02861**Tim Dettmers, Mike Lewis, Sam Shleifer, et al.*2021 · 181 citations*](https://md.research.elamur.ai/free/en/papers/dettmers-2021-8-bit-optimizers-block-wise/)
53. **[KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation](https://md.research.elamur.ai/free/en/papers/chen-2020-kgpt-knowledge-grounded-pre-training/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenhu Chen, Yu Su, Xifeng Yan, et al.*2020 · 174 citations*
54. **[Towards Controllable Biases in Language Generation](https://md.research.elamur.ai/free/en/papers/sheng-2020-controllable-biases-language-generation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Emily Sheng, Kai-Wei Chang, Prem Natarajan, et al.*2020 · 171 citations*
55. **[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*
56. **[Few-Shot Text Generation with Natural Language Instructions](https://md.research.elamur.ai/free/en/papers/schick-2021-few-shot-text-generation-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Timo Schick, Hinrich Schütze*2021 · 167 citations*
57. **[Language Models as Agent Models](https://md.research.elamur.ai/free/en/papers/andreas-2022-language-models-as-agent-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jacob Andreas*2022 · 165 citations*
58. **[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*
59. **[Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding](https://md.research.elamur.ai/free/en/papers/dziri-2021-neural-path-hunter-reducing-hallucination/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nouha Dziri, Andrea Madotto, Osmar Zaïane, et al.*2021 · 152 citations*
60. **[Measure and Improve Robustness in NLP Models: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2022-measure-improve-robustness-nlp-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xuezhi Wang, Haohan Wang, Diyi Yang*2022 · 148 citations*
61. [**Efficient Training of Language Models to Fill in the Middle arXiv:2207.14255**Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, et al.*2022 · 144 citations*](https://md.research.elamur.ai/free/en/papers/bavarian-2022-efficient-training-language-models-fill/)
62. **[RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction](https://md.research.elamur.ai/free/en/papers/chia-2022-relationprompt-leveraging-prompts-generate-synthetic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yew Ken Chia, Lidong Bing, Soujanya Poria, et al.*2022 · 141 citations*
63. **[Increasing Faithfulness in Knowledge-Grounded Dialogue with Controllable Features](https://md.research.elamur.ai/free/en/papers/rashkin-2021-increasing-faithfulness-knowledge-grounded-dialogue/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hannah Rashkin, David Reitter, Gaurav Singh Tomar, et al.*2021 · 141 citations*
64. **[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*
65. **[Detoxifying Language Models Risks Marginalizing Minority Voices](https://md.research.elamur.ai/free/en/papers/xu-2021-detoxifying-language-models-risks-marginalizing/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Albert Xu, Eshaan Pathak, Eric Wallace, et al.*2021 · 137 citations*
66. [**Secrets of RLHF in Large Language Models Part I: PPO arXiv:2307.04964**Rui Zheng, Shihan Dou, Songyang Gao, et al.*2023 · 135 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-secrets-rlhf-large-language-models/)
67. **[It's Morphin' Time! Combating Linguistic Discrimination with Inflectional Perturbations](https://md.research.elamur.ai/free/en/papers/tan-2020-it-s-morphin-time-combating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Samson Tan, Shafiq Joty, Min-Yen Kan, et al.*2020 · 131 citations*
68. **[Controllable Natural Language Generation with Contrastive Prefixes](https://md.research.elamur.ai/free/en/papers/qian-2022-controllable-natural-language-generation-contrastive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jing Qian, Li Dong, Yelong Shen, et al.*2022 · 130 citations*
69. **[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*
70. [**Chain of Hindsight Aligns Language Models with Feedback arXiv:2302.02676**Hao Liu, Carmelo Sferrazza, Pieter Abbeel*2023 · 126 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-chain-hindsight-aligns-language-models/)
71. [**PEER: A Collaborative Language Model arXiv:2208.11663**Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, et al.*2022 · 126 citations*](https://md.research.elamur.ai/free/en/papers/schick-2022-peer-collaborative-language-model/)
72. [**Large Language Model Alignment: A Survey arXiv:2309.15025**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
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