---
title: "Who cites Sumanth Dathathri"
description: "712 papers of the Elamur Research library cite the works of Sumanth Dathathri, found in their bibliographies; the most cited first."
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---
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# Who cites Sumanth Dathathri

749 citations of their works from 712 papers of our library (of the 140,884 whose bibliographies we read) · 99 with a page here, the most cited first · [← Sumanth Dathathri](https://md.research.elamur.ai/free/en/authors/sumanth-dathathri/)

1. [**GPT-4 Technical Report**OpenAI, Josh Achiam, Steven Adler, et al.*2023 · 12,694 citations*](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
2. [**Training language models to follow instructions with human feedback**Long Ouyang, Jeff Wu, Xu Jiang, et al.*2022 · 5,770 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2022-training-language-models-follow-instructions/)
3. [**A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,221 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/)
4. [**Gemini: A Family of Highly Capable Multimodal Models**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 2,951 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/)
5. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
6. [**PaLM: Scaling Language Modeling with Pathways**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 2,603 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
7. [**ReAct: Synergizing Reasoning and Acting in Language Models**Shunyu Yao, Jeffrey Zhao, Dian Yu, et al.*2022 · 2,493 citations*](https://md.research.elamur.ai/free/en/papers/yao-2022-react-synergizing-reasoning-acting-language/)
8. [**OPT: Open Pre-trained Transformer Language Models**Susan Zhang, Stephen Roller, Naman Goyal, et al.*2022 · 1,709 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/)
9. [**A Comprehensive Overview of Large Language Models**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations*](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
10. [**RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models**Samuel Gehman, Suchin Gururangan, Maarten Sap, et al.*2020 · 1,277 citations*](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/)
11. [**Training Compute-Optimal Large Language Models**Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al.*2022 · 1,110 citations*](https://md.research.elamur.ai/free/en/papers/hoffmann-2022-training-compute-optimal-large-language/)
12. [**Bias and Fairness in Large Language Models: A Survey**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,072 citations*](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/)
13. [**PaLM 2 Technical Report**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 971 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
14. [**Constitutional AI: Harmlessness from AI Feedback**Yuntao Bai, Saurav Kadavath, Sandipan Kundu, et al.*2022 · 957 citations*](https://md.research.elamur.ai/free/en/papers/bai-2022-constitutional-ai-harmlessness-ai-feedback/)
15. [**Universal and Transferable Adversarial Attacks on Aligned Language Models**Andy Zou, Zifan Wang, Nicholas Carlini, et al.*2023 · 951 citations*](https://md.research.elamur.ai/free/en/papers/zou-2023-universal-transferable-adversarial-attacks-aligned/)
16. [**CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society**Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, et al.*2023 · 689 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-camel-communicative-agents-mind/)
17. [**Red Teaming Language Models with Language Models**Ethan Perez, Saffron Huang, Francis Song, et al.*2022 · 665 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-red-teaming-language-models-language/)
18. [**Jailbroken: How Does LLM Safety Training Fail?**Alexander Wei, Nika Haghtalab, Jacob Steinhardt*2023 · 659 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-jailbroken-llm-safety-training-fail/)
19. [**BloombergGPT: A Large Language Model for Finance**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 608 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
20. [**Role-Play with Large Language Models**Murray Shanahan, Kyle McDonell, Laria Reynolds*2023 · 606 citations*](https://md.research.elamur.ai/free/en/papers/shanahan-2023-role-play-large-language-models/)
21. [**Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model**Shaden Smith, Mostofa Patwary, Brandon Norick, et al.*2022 · 595 citations*](https://md.research.elamur.ai/free/en/papers/smith-2022-using-deepspeed-megatron-train-megatron/)
22. [**The Flan Collection: Designing Data and Methods for Effective Instruction Tuning**Shayne Longpre, Le Hou, Tu Vu, et al.*2023 · 590 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/)
23. [**Jailbreaking Black Box Large Language Models in Twenty Queries**Patrick Chao, Alexander Robey, Edgar Dobriban, et al.*2023 · 533 citations*](https://md.research.elamur.ai/free/en/papers/chao-2023-jailbreaking-black-box-large-language/)
24. [**OpenAssistant Conversations — Democratizing Large Language Model Alignment**Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, et al.*2023 · 505 citations*](https://md.research.elamur.ai/free/en/papers/kopf-2023-openassistant-conversations-democratizing-large-language/)
25. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 474 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
26. [**Inference-Time Intervention: Eliciting Truthful Answers from a Language Model**Kenneth Li, Oam Patel, Fernanda Viegas, et al.*2023 · 473 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-inference-time-intervention-eliciting-truthful/)
27. [**Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned**Deep Ganguli, Liane Lovitt, Jackson Kernion, et al.*2022 · 456 citations*](https://md.research.elamur.ai/free/en/papers/ganguli-2022-red-teaming-language-models-reduce/)
28. [**GeDi: Generative Discriminator Guided Sequence Generation**Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, et al.*2021 · 444 citations*](https://md.research.elamur.ai/free/en/papers/krause-2021-gedi-generative-discriminator-guided-sequence/)
29. [**The Falcon Series of Open Language Models**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 436 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/)
30. [**AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback**Yann Dubois, Xuechen Li, Rohan Taori, et al.*2023 · 423 citations*](https://md.research.elamur.ai/free/en/papers/dubois-2023-alpacafarm-simulation-framework-methods-that/)
31. [**Diffusion-LM Improves Controllable Text Generation**Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, et al.*2022 · 421 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-diffusion-lm-improves-controllable-text/)
32. [**Ethical and social risks of harm from Language Models**Laura Weidinger, John Mellor, Maribeth Rauh, et al.*2021 · 421 citations*](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/)
33. [**Yi: Open Foundation Models by 01.AI**Alex Young, Bei Chen, Chao Li, et al.*2024 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/)
34. [**DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts**Alisa Liu, Maarten Sap, Ximing Lu, et al.*2021 · 399 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-dexperts-decoding-time-controlled-text/)
35. [**Whose Opinions Do Language Models Reflect?**Shibani Santurkar, Esin Durmus, Faisal Ladhak, et al.*2023 · 390 citations*](https://md.research.elamur.ai/free/en/papers/santurkar-2023-whose-opinions-language-models-reflect/)
36. [**Editing Models with Task Arithmetic**Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, et al.*2022 · 374 citations*](https://md.research.elamur.ai/free/en/papers/ilharco-2022-editing-models-task-arithmetic/)
37. [**Augmented Language Models: a Survey**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 362 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/)
38. [**Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback**Baolin Peng, Michel Galley, Pengcheng He, et al.*2023 · 354 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-check-facts-try-again-improving/)
39. [**Towards Understanding Sycophancy in Language Models**Mrinank Sharma, Meg Tong, Tomasz Korbak, et al.*2023 · 354 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2023-towards-understanding-sycophancy-language-models/)
40. [**BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset**Jiaming Ji, Mickel Liu, Juntao Dai, et al.*2023 · 351 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-beavertails-towards-improved-safety-alignment/)
41. [**Reformulating Unsupervised Style Transfer as Paraphrase Generation**Kalpesh Krishna, John Wieting, Mohit Iyyer*2020 · 348 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2020-reformulating-unsupervised-style-transfer-as/)
42. [**Improving alignment of dialogue agents via targeted human judgements**Amelia Glaese, Nat McAleese, Maja Trębacz, et al.*2022 · 346 citations*](https://md.research.elamur.ai/free/en/papers/glaese-2022-improving-alignment-dialogue-agents-targeted/)
43. [**CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 315 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/)
44. [**Challenges and Applications of Large Language Models**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 300 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
45. [**Evaluating Verifiability in Generative Search Engines**Nelson Liu, Tianyi Zhang, Percy Liang*2023 · 294 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-verifiability-generative-search-engines/)
46. [**Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models**Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, et al.*2021 · 271 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-polyjuice-generating-counterfactuals-explaining-evaluating/)
47. [**Scaling Laws for Reward Model Overoptimization**Leo Gao, John Schulman, Jacob Hilton*2022 · 266 citations*](https://md.research.elamur.ai/free/en/papers/gao-2022-scaling-laws-reward-model-overoptimization/)
48. [**Towards Understanding and Mitigating Social Biases in Language Models**Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, et al.*2021 · 263 citations*](https://md.research.elamur.ai/free/en/papers/liang-2021-towards-understanding-mitigating-social-biases/)
49. [**Towards Measuring the Representation of Subjective Global Opinions in Language Models**Esin Durmus, Karina Nguyen, Thomas I. Liao, et al.*2023 · 262 citations*](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/)
50. [**A Holistic Approach to Undesired Content Detection in the Real World**Todor Markov, Chong Zhang, Sandhini Agarwal, et al.*2022 · 254 citations*](https://md.research.elamur.ai/free/en/papers/markov-2022-holistic-approach-undesired-content-detection/)
51. [**Societal Biases in Language Generation: Progress and Challenges**Emily Sheng, Kai-Wei Chang, Prem Natarajan, et al.*2021 · 252 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2021-societal-biases-language-generation-progress/)
52. [**DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models**Boxin Wang, Weixin Chen, Hengzhi Pei, et al.*2023 · 246 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/)
53. [**Automatic Detection of Machine Generated Text: A Critical Survey**Ganesh Jawahar, Muhammad Abdul-Mageed, Laks Lakshmanan V.S.*2020 · 243 citations*](https://md.research.elamur.ai/free/en/papers/jawahar-2020-automatic-detection-machine-generated-text/)
54. [**Language Models can Solve Computer Tasks**Geunwoo Kim, Pierre Baldi, Stephen McAleer*2023 · 239 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-language-models-solve-computer-tasks/)
55. [**RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment**Hanze Dong, Wei Xiong, Deepanshu Goyal, et al.*2023 · 239 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-raft-reward-ranked-finetuning-generative/)
56. [**FUDGE: Controlled Text Generation With Future Discriminators**Kevin Yang, Dan Klein*2021 · 234 citations*](https://md.research.elamur.ai/free/en/papers/yang-2021-fudge-controlled-text-generation-future/)
57. [**XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models**Paul Rottger, Hannah Rose Kirk, Bertie Vidgen, et al.*2023 · 227 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2023-xstest-test-suite-identifying-exaggerated/)
58. [**TrustLLM: Trustworthiness in Large Language Models**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
59. [**Baseline Defenses for Adversarial Attacks Against Aligned Language Models**Neel Jain, Avi Schwarzschild, Yuxin Wen, et al.*2023 · 224 citations*](https://md.research.elamur.ai/free/en/papers/jain-2023-baseline-defenses-adversarial-attacks-against/)
60. [**Steering Language Models With Activation Engineering**Alexander Matt Turner, Lisa Thiergart, Gavin Leech, et al.*2023 · 220 citations*](https://md.research.elamur.ai/free/en/papers/turner-2023-steering-language-models-activation-engineering/)
61. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 219 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
62. [**Fine-Grained Human Feedback Gives Better Rewards for Language Model Training**Zeqiu Wu, Yushi Hu, Weijia Shi, et al.*2023 · 212 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-fine-grained-human-feedback-gives/)
63. [**Reinforced Self-Training (ReST) for Language Modeling**Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al.*2023 · 209 citations*](https://md.research.elamur.ai/free/en/papers/gulcehre-2023-reinforced-self-training-rest-language/)
64. [**Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning**Pan Lu, Liang Qiu, Kai-Wei Chang, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-dynamic-prompt-learning-policy-gradient/)
65. [**Safe RLHF: Safe Reinforcement Learning from Human Feedback**Josef Dai, Xuehai Pan, Ruiyang Sun, et al.*2023 · 207 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-safe-rlhf-safe-reinforcement-learning/)
66. [**Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP**Timo Schick, Sahana Udupa, Hinrich Schutze*2021 · 207 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-self-diagnosis-self-debiasing-proposal/)
67. [**Generating Training Data with Language Models: Towards Zero-Shot Language Understanding**Yu Meng, Jiaxin Huang, Yu Zhang, et al.*2022 · 206 citations*](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/)
68. [**Challenges in Detoxifying Language Models**Johannes Welbl, Amelia Glaese, Jonathan Uesato, et al.*2021 · 206 citations*](https://md.research.elamur.ai/free/en/papers/welbl-2021-challenges-detoxifying-language-models/)
69. [**Authorship Attribution for Neural Text Generation**Adaku Uchendu, Thai Le, Kai Shu, et al.*2020 · 195 citations*](https://md.research.elamur.ai/free/en/papers/uchendu-2020-authorship-attribution-neural-text-generation/)
70. [**Multilingual Jailbreak Challenges in Large Language Models**Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, et al.*2023 · 183 citations*](https://md.research.elamur.ai/free/en/papers/deng-2023-multilingual-jailbreak-challenges-large-language/)
71. [**Generating Sequences by Learning to Self-Correct**Sean Welleck, Ximing Lu, Peter West, et al.*2022 · 170 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2022-generating-sequences-learning-self-correct/)
72. [**Towards Controllable Biases in Language Generation**Emily Sheng, Kai-Wei Chang, Prem Natarajan, et al.*2020 · 166 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2020-controllable-biases-language-generation/)
73. [**Quark: Controllable Text Generation with Reinforced Unlearning**Ximing Lu, Sean Welleck, Jack Hessel, et al.*2022 · 164 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-quark-controllable-text-generation-reinforced/)
74. [**Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision**Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.*2023 · 163 citations*](https://md.research.elamur.ai/free/en/papers/burns-2023-weak-strong-generalization-eliciting-strong/)
75. [**Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation**Yangsibo Huang, Samyak Gupta, Mengzhou Xia, et al.*2023 · 162 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-catastrophic-jailbreak-open-source-llms/)
76. [**GPTFUZZER: Red Teaming Large Language Models with Auto-Generated Jailbreak Prompts**Jiahao Yu, Xingwei Lin, Zheng Yu, et al.*2023 · 162 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-gptfuzzer-red-teaming-large-language/)
77. [**GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher**Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-gpt-4-too-smart-safe/)
78. [**SmoothLLM: Defending Large Language Models Against Jailbreaking Attacks**Alexander Robey, Eric Wong, Hamed Hassani, et al.*2023 · 157 citations*](https://md.research.elamur.ai/free/en/papers/robey-2023-smoothllm-defending-large-language-models/)
79. [**Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models**Bernd Bohnet, Vinh Q. Tran, Pat Verga, et al.*2022 · 136 citations*](https://md.research.elamur.ai/free/en/papers/bohnet-2022-attributed-question-answering-evaluation-modeling/)
80. [**NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics**Ximing Lu, Sean Welleck, Peter West, et al.*2022 · 136 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-neurologic-esque-decoding-constrained-text/)
81. [**A Roadmap to Pluralistic Alignment**Taylor Sorensen, Jared Moore, Jillian Fisher, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/)
82. [**SLiC-HF: Sequence Likelihood Calibration with Human Feedback**Yao Zhao, Rishabh Joshi, Tianqi Liu, et al.*2023 · 135 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-slic-hf-sequence-likelihood-calibration/)
83. [**InFoBench: Evaluating Instruction Following Ability in Large Language Models**Yiwei Qin, Kaiqiang Song, Yebowen Hu, et al.*2024 · 133 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-infobench-evaluating-instruction-following-ability/)
84. [**Understanding the Effects of RLHF on LLM Generalisation and Diversity**Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, et al.*2023 · 131 citations*](https://md.research.elamur.ai/free/en/papers/kirk-2023-understanding-effects-rlhf-llm-generalisation/)
85. [**Extracting Latent Steering Vectors from Pretrained Language Models**Nishant Subramani, Nivedita Suresh, Matthew Peters*2022 · 131 citations*](https://md.research.elamur.ai/free/en/papers/subramani-2022-extracting-latent-steering-vectors-pretrained/)
86. [**Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback**Yao Fu, Hao Peng, Tushar Khot, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/fu-2023-improving-language-model-negotiation-self/)
87. [**SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding**Zhangchen Xu, Fengqing Jiang, Luyao Niu, et al.*2024 · 126 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-safedecoding-defending-against-jailbreak-attacks/)
88. [**Large Language Model Alignment: A Survey**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
89. [**Pretraining Language Models with Human Preferences**Tomasz Korbak, Kejian Shi, Angelica Chen, et al.*2023 · 117 citations*](https://md.research.elamur.ai/free/en/papers/korbak-2023-pretraining-language-models-human-preferences/)
90. [**Certifying LLM Safety against Adversarial Prompting**Aounon Kumar, Chirag Agarwal, Suraj Srinivas, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2023-certifying-llm-safety-against-adversarial/)
91. [**The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs**Laura Ruis, Akbir Khan, Stella Biderman, et al.*2022 · 86 citations*](https://md.research.elamur.ai/free/en/papers/ruis-2022-goldilocks-pragmatic-understanding-fine-tuning/)
92. [**Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation**Patrick Fernandes, Aman Madaan, Emmy Liu, et al.*2023 · 84 citations*](https://md.research.elamur.ai/free/en/papers/fernandes-2023-bridging-gap-survey-integrating-human/)
93. [**Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla**Tom Lieberum, Matthew Rahtz, Janos Kramar, et al.*2023 · 51 citations*](https://md.research.elamur.ai/free/en/papers/lieberum-2023-circuit-analysis-interpretability-scale-evidence/)
94. [**ChatGPT vs Human-authored Text: Insights into Controllable Text Summarization and Sentence Style Transfer**Dongqi Liu, Vera Demberg*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-chatgpt-vs-human-authored-text/)
95. [**Facilitating the Communication of Politeness through Fine-Grained Paraphrasing**Liye Fu, Susan Fussell, Cristian Danescu-Niculescu-Mizil*2020 · 39 citations*](https://md.research.elamur.ai/free/en/papers/fu-2020-facilitating-communication-politeness-through-fine/)
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