---
title: "Safety Research — papers, page 2 — Elamur Research"
description: "Safety Research: 221 papers of the Elamur Research library · page 2 of 3 · 26–125, the most cited first — our summary of each, its ideas…"
canonical: "https://research.elamur.ai/free/en/shelves/social-sciences/safety-research/page/2/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/social-sciences/safety-research/page/2/)

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# 🤝 Safety Research

221 papers · page 2 of 3 · 26–125 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/)

Safety Research is a subfield of Social Sciences in the OpenAlex classification of science. The topics most of its papers here are on: «Ethics and Social Impacts of AI», «Academic integrity and plagiarism», «Disability Rights and Representation».

## Papers (221)

[← prev](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) [1](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) **2** [3](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/page/3/) [next →](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/page/3/)

26. [**Persistent Anti-Muslim Bias in Large Language Models**Abubakar Abid, Maheen Farooqi, James Zou*2021 · 644 citations*](https://md.research.elamur.ai/free/en/papers/abid-2021-persistent-anti-muslim-bias-large/)
27. [**Evaluating Gender Bias in Machine Translation**Gabriel Stanovsky, Noah A. Smith, Luke Zettlemoyer*2019 · 589 citations*](https://md.research.elamur.ai/free/en/papers/stanovsky-2019-evaluating-gender-bias-machine-translation/)
28. [**Measuring Bias in Contextualized Word Representations**Keita Kurita, Nidhi Vyas, Ayush Pareek, et al.*2019 · 588 citations*](https://md.research.elamur.ai/free/en/papers/kurita-2019-measuring-bias-contextualized-word-representations/)
29. [**Racial Bias in Hate Speech and Abusive Language Detection Datasets**Thomas Davidson, Debasmita Bhattacharya, Ingmar Weber*2019 · 580 citations*](https://md.research.elamur.ai/free/en/papers/davidson-2019-racial-bias-hate-speech-abusive/)
30. [**Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems**Svetlana Kiritchenko, Saif Mohammad*2018 · 574 citations*](https://md.research.elamur.ai/free/en/papers/kiritchenko-2018-examining-gender-race-bias-two/)
31. [**A Categorical Archive of ChatGPT Failures**Ali Borji*2023 · 558 citations*](https://md.research.elamur.ai/free/en/papers/borji-2023-categorical-archive-chatgpt-failures/)
32. [**Release Strategies and the Social Impacts of Language Models**Irene Solaiman, Miles Brundage, Jack Clark, et al.*2019 · 545 citations*](https://md.research.elamur.ai/free/en/papers/solaiman-2019-release-strategies-social-impacts-language/)
33. [**The Social Impact of Natural Language Processing**Dirk Hovy, Shannon L. Spruit*2016 · 518 citations*](https://md.research.elamur.ai/free/en/papers/hovy-2016-social-impact-natural-language-processing/)
34. [**Learning Gender-Neutral Word Embeddings**Jieyu Zhao, Yichao Zhou, Zeyu Li, et al.*2018 · 514 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2018-learning-gender-neutral-word-embeddings/)
35. [**Gender Bias in Contextualized Word Embeddings**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2019 · 505 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2019-gender-bias-contextualized-word-embeddings/)
36. [**BBQ: A hand-built bias benchmark for question answering**Alicia Parrish, Angelica Chen, Nikita Nangia, et al.*2022 · 488 citations*](https://md.research.elamur.ai/free/en/papers/parrish-2022-bbq-hand-built-bias-benchmark/)
37. [**Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection**Shauli Ravfogel, Yanai Elazar, Hila Gonen, et al.*2020 · 480 citations*](https://md.research.elamur.ai/free/en/papers/ravfogel-2020-null-it-out-guarding-protected/)
38. [**Reducing Gender Bias in Abusive Language Detection**Ji Ho Park, Jamin Shin, Pascale Fung*2018 · 465 citations*](https://md.research.elamur.ai/free/en/papers/park-2018-reducing-gender-bias-abusive-language/)
39. [**Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned**Deep Ganguli, Liane Lovitt, Jackson Kernion, et al.*2022 · 460 citations*](https://md.research.elamur.ai/free/en/papers/ganguli-2022-red-teaming-language-models-reduce/)
40. [**Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models**Matthew Dahl, Varun Magesh, Mirac Suzgun, et al.*2024 · 432 citations*](https://md.research.elamur.ai/free/en/papers/dahl-2024-large-legal-fictions-profiling-legal/)
41. [**Ethical and social risks of harm from Language Models**Laura Weidinger, John Mellor, Maribeth Rauh, et al.*2021 · 429 citations*](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/)
42. [**Gender and Dialect Bias in YouTube's Automatic Captions**Rachael Tatman*2017 · 414 citations*](https://md.research.elamur.ai/free/en/papers/tatman-2017-gender-dialect-bias-youtube-s/)
43. [**Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them**Hila Gonen, Yoav Goldberg*2019 · 413 citations*](https://md.research.elamur.ai/free/en/papers/gonen-2019-lipstick-pig-debiasing-methods-cover/)
44. [**Social Biases in NLP Models as Barriers for Persons with Disabilities**Ben Hutchinson, Vinodkumar Prabhakaran, Emily Denton, et al.*2020 · 404 citations*](https://md.research.elamur.ai/free/en/papers/hutchinson-2020-social-biases-nlp-models-as/)
45. [**Gender Bias in Neural Natural Language Processing**Kaiji Lu, Piotr Mardziel, Fangjing Wu, et al.*2018 · 394 citations*](https://md.research.elamur.ai/free/en/papers/lu-2018-gender-bias-neural-natural-language/)
46. [**Whose Opinions Do Language Models Reflect?**Shibani Santurkar, Esin Durmus, Faisal Ladhak, et al.*2023 · 392 citations*](https://md.research.elamur.ai/free/en/papers/santurkar-2023-whose-opinions-language-models-reflect/)
47. [**Representation Engineering: A Top-Down Approach to AI Transparency**Andy Zou, Long Phan, Sarah Chen, et al.*2023 · 386 citations*](https://md.research.elamur.ai/free/en/papers/zou-2023-representation-engineering-top-down-approach/)
48. [**Gender and Representation Bias in GPT-3 Generated Stories**Li Lucy, David Bamman*2021 · 385 citations*](https://md.research.elamur.ai/free/en/papers/lucy-2021-gender-representation-bias-gpt-3/)
49. [**Aligning AI With Shared Human Values**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 383 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-aligning-ai-shared-human-values/)
50. [**Green AI**Roy Schwartz, Jesse Dodge, Noah A. Smith, et al.*2019 · 370 citations*](https://md.research.elamur.ai/free/en/papers/schwartz-2019-green-ai/)
51. [**Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection**Maarten Sap, Swabha Swayamdipta, Laura Vianna, et al.*2022 · 365 citations*](https://md.research.elamur.ai/free/en/papers/sap-2022-annotators-attitudes-how-annotator-beliefs/)
52. [**Identifying and Reducing Gender Bias in Word-Level Language Models**Shikha Bordia, Samuel R. Bowman*2019 · 364 citations*](https://md.research.elamur.ai/free/en/papers/bordia-2019-identifying-reducing-gender-bias-word/)
53. [**Adversarial Removal of Demographic Attributes from Text Data**Yanai Elazar, Yoav Goldberg*2018 · 357 citations*](https://md.research.elamur.ai/free/en/papers/elazar-2018-adversarial-removal-demographic-attributes-text/)
54. [**Towards Understanding Sycophancy in Language Models**Mrinank Sharma, Meg Tong, Tomasz Korbak, et al.*2023 · 355 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2023-towards-understanding-sycophancy-language-models/)
55. [**BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset**Jiaming Ji, Mickel Liu, Juntao Dai, et al.*2023 · 352 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-beavertails-towards-improved-safety-alignment/)
56. [**Toxicity in chatgpt: Analyzing persona-assigned language models**Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, et al.*2023 · 346 citations*](https://md.research.elamur.ai/free/en/papers/deshpande-2023-toxicity-chatgpt-analyzing-persona-assigned/)
57. [**Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings**Thomas Manzini, Lim Yao Chong, Alan W Black, et al.*2019 · 332 citations*](https://md.research.elamur.ai/free/en/papers/manzini-2019-black-is-criminal-as-caucasian/)
58. [**Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!**Xiangyu Qi, Yi Zeng, Tinghao Xie, et al.*2023 · 331 citations*](https://md.research.elamur.ai/free/en/papers/qi-2023-fine-tuning-aligned-language-models/)
59. [**Gender Bias in Machine Translation**Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, et al.*2021 · 310 citations*](https://md.research.elamur.ai/free/en/papers/savoldi-2021-gender-bias-machine-translation/)
60. [**Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?**Peter Hase, Mohit Bansal*2020 · 309 citations*](https://md.research.elamur.ai/free/en/papers/hase-2020-evaluating-explainable-ai-which-algorithmic/)
61. [**Discovering Language Model Behaviors with Model-Written Evaluations**Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, et al.*2022 · 307 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-discovering-language-model-behaviors-model/)
62. [**From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models**Shangbin Feng, Chan Young Park, Yuhan Liu, et al.*2023 · 306 citations*](https://md.research.elamur.ai/free/en/papers/feng-2023-pretraining-data-language-models-downstream/)
63. [**Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model**Marzieh Mozafari, Reza Farahbakhsh, Noel Crespi*2020 · 305 citations*](https://md.research.elamur.ai/free/en/papers/mozafari-2020-hate-speech-detection-racial-bias/)
64. [**Towards Debiasing Fact Verification Models**Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, et al.*2019 · 296 citations*](https://md.research.elamur.ai/free/en/papers/schuster-2019-debiasing-fact-verification-models/)
65. [**Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study**Yong Cao, Li Zhou, Seolhwa Lee, et al.*2023 · 278 citations*](https://md.research.elamur.ai/free/en/papers/cao-2023-assessing-cross-cultural-alignment-between/)
66. [**Having Beer after Prayer? Measuring Cultural Bias in Large Language Models**Tarek Naous, Michael J. Ryan, Alan Ritter, et al.*2023 · 268 citations*](https://md.research.elamur.ai/free/en/papers/naous-2023-having-beer-after-prayer-measuring/)
67. [**Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools**Varun Magesh, Faiz Surani, Matthew Dahl, et al.*2024 · 267 citations*](https://md.research.elamur.ai/free/en/papers/magesh-2024-hallucination-free-assessing-reliability-leading/)
68. [**Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study**Yi Liu, Gelei Deng, Zhengzi Xu, et al.*2023 · 266 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-jailbreaking-chatgpt-prompt-engineering-empirical/)
69. [**Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation**Emily Dinan, Angela Fan, Adina Williams, et al.*2020 · 266 citations*](https://md.research.elamur.ai/free/en/papers/dinan-2020-queens-are-powerful-too-mitigating/)
70. [**Towards Understanding and Mitigating Social Biases in Language Models**Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, et al.*2021 · 265 citations*](https://md.research.elamur.ai/free/en/papers/liang-2021-towards-understanding-mitigating-social-biases/)
71. [**Evaluating the Underlying Gender Bias in Contextualized Word Embeddings**Christine Basta, Marta R. Costa-jussà, Noe Casas*2019 · 265 citations*](https://md.research.elamur.ai/free/en/papers/basta-2019-evaluating-underlying-gender-bias-contextualized/)
72. [**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/)
73. [**Towards Debiasing Sentence Representations**Paul Pu Liang, Irene Mengze Li, Emily Zheng, et al.*2020 · 259 citations*](https://md.research.elamur.ai/free/en/papers/liang-2020-debiasing-sentence-representations/)
74. [**Decolonising Speech and Language Technology**Steven Bird*2020 · 254 citations*](https://md.research.elamur.ai/free/en/papers/bird-2020-decolonising-speech-language-technology/)
75. [**Societal Biases in Language Generation: Progress and Challenges**Emily Sheng, Kai-Wei Chang, Prem Natarajan, et al.*2021 · 253 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2021-societal-biases-language-generation-progress/)
76. [**Measuring and Reducing Gendered Correlations in Pre-trained Models**Kellie Webster, Xuezhi Wang, Ian Tenney, et al.*2020 · 253 citations*](https://md.research.elamur.ai/free/en/papers/webster-2020-measuring-reducing-gendered-correlations-pre/)
77. [**Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings**Rishi Bommasani, Kelly Davis, Claire Cardie*2020 · 248 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2020-interpreting-pretrained-contextualized-representations-reductions/)
78. [**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/)
79. [**Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity**Terry Yue Zhuo, Yujin Huang, Chunyang Chen, et al.*2023 · 246 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2023-red-teaming-chatgpt-jailbreaking-bias/)
80. [**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/)
81. [**Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models**Myra Cheng, Esin Durmus, Dan Jurafsky*2023 · 236 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2023-marked-personas-natural-language-prompts/)
82. [**ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning**Petter Tornberg*2023 · 231 citations*](https://md.research.elamur.ai/free/en/papers/tornberg-2023-chatgpt-4-outperforms-experts-crowd/)
83. [**Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies**Sunipa Dev, Masoud Monajatipoor, Anaelia Ovalle, et al.*2021 · 228 citations*](https://md.research.elamur.ai/free/en/papers/dev-2021-harms-gender-exclusivity-challenges-non/)
84. [**Learning to Deceive with Attention-Based Explanations**Danish Pruthi, Mansi Gupta, Bhuwan Dhingra, et al.*2020 · 227 citations*](https://md.research.elamur.ai/free/en/papers/pruthi-2020-learning-deceive-attention-based-explanations/)
85. [**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/)
86. [**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 · 220 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
87. [**Systematic Inequalities in Language Technology Performance across the World's Languages**Damian Blasi, Antonios Anastasopoulos, Graham Neubig*2022 · 214 citations*](https://md.research.elamur.ai/free/en/papers/blasi-2022-systematic-inequalities-language-technology-performance/)
88. [**An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models**Nicholas Meade, Elinor Poole-Dayan, Siva Reddy*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/meade-2022-empirical-survey-effectiveness-debiasing-techniques/)
89. [**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/)
90. [**Prompting GPT-3 To Be Reliable**Chenglei Si, Zhe Gan, Zhengyuan Yang, et al.*2022 · 207 citations*](https://md.research.elamur.ai/free/en/papers/si-2022-prompting-gpt-3-reliable/)
91. [**Challenges in Detoxifying Language Models**Johannes Welbl, Amelia Glaese, Jonathan Uesato, et al.*2021 · 207 citations*](https://md.research.elamur.ai/free/en/papers/welbl-2021-challenges-detoxifying-language-models/)
92. [**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/)
93. [**“Kelly is a Warm Person, Joseph is a Role Model”: Gender Biases in LLM-Generated Reference Letters**Yixin Wan, George Pu, Jiao Sun, et al.*2023 · 206 citations*](https://md.research.elamur.ai/free/en/papers/wan-2023-kelly-is-warm-person-joseph/)
94. [**Contextualizing Hate Speech Classifiers with Post-hoc Explanation**Brendan Kennedy, Xisen Jin, Aida Mostafazadeh Davani, et al.*2020 · 204 citations*](https://md.research.elamur.ai/free/en/papers/kennedy-2020-contextualizing-hate-speech-classifiers-post/)
95. [**HateCheck: Functional Tests for Hate Speech Detection Models**Paul Röttger, Bertie Vidgen, Dong Nguyen, et al.*2021 · 203 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2021-hatecheck-functional-tests-hate-speech/)
96. [**Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview**Deven Shah, H. Andrew Schwartz, Dirk Hovy*2019 · 202 citations*](https://md.research.elamur.ai/free/en/papers/shah-2019-predictive-biases-natural-language-processing/)
97. [**How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs**Yi Zeng, Hongpeng Lin, Jingwen Zhang, et al.*2024 · 201 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2024-how-johnny-can-persuade-llms/)
98. [**Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias**Yue Yu, Yuchen Zhuang, Jieyu Zhang, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-large-language-model-attributed-training/)
99. [**Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts**Yue Guo, Yi Yang, Ahmed Abbasi*2022 · 191 citations*](https://md.research.elamur.ai/free/en/papers/guo-2022-auto-debias-debiasing-masked-language/)
100. [**Challenges in Automated Debiasing for Toxic Language Detection**Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, et al.*2021 · 190 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2021-challenges-automated-debiasing-toxic-language/)
101. [**HONEST: Measuring Hurtful Sentence Completion in Language Models**Debora Nozza, Federico Bianchi, Dirk Hovy*2021 · 190 citations*](https://md.research.elamur.ai/free/en/papers/nozza-2021-honest-measuring-hurtful-sentence-completion/)
102. [**A Just and Comprehensive Strategy for Using NLP to Address Online Abuse**David Jurgens, Libby Hemphill, Eshwar Chandrasekharan*2019 · 189 citations*](https://md.research.elamur.ai/free/en/papers/jurgens-2019-just-comprehensive-strategy-nlp-address/)
103. [**SemEval-2023 Task 10: Explainable Detection of Online Sexism**Hannah Kirk, Wenjie Yin, Bertie Vidgen, et al.*2023 · 185 citations*](https://md.research.elamur.ai/free/en/papers/kirk-2023-semeval-2023-task-10-explainable/)
104. [**Preference Ranking Optimization for Human Alignment**Feifan Song, Bowen Yu, Minghao Li, et al.*2023 · 183 citations*](https://md.research.elamur.ai/free/en/papers/song-2023-preference-ranking-optimization-human-alignment/)
105. [**On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning**Omar Shaikh, Hongxin Zhang, William Held, et al.*2023 · 180 citations*](https://md.research.elamur.ai/free/en/papers/shaikh-2023-second-thought-let-s-not/)
106. [**Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation**Shivalika Singh, Angelika Romanou, Clémentine Fourrier, et al.*2025 · 179 citations*](https://md.research.elamur.ai/free/en/papers/singh-2025-global-mmlu-understanding-addressing-cultural/)
107. [**It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution**Rowan Hall Maudslay, Hila Gonen, Ryan Cotterell, et al.*2019 · 179 citations*](https://md.research.elamur.ai/free/en/papers/maudslay-2019-it-s-all-name-mitigating/)
108. [**Investigating Cultural Alignment of Large Language Models**Badr AlKhamissi, Muhammad ElNokrashy, Mai Alkhamissi, et al.*2024 · 177 citations*](https://md.research.elamur.ai/free/en/papers/alkhamissi-2024-investigating-cultural-alignment-large-language/)
109. [**Debiasing Pre-trained Contextualised Embeddings**Masahiro Kaneko, Danushka Bollegala*2021 · 174 citations*](https://md.research.elamur.ai/free/en/papers/kaneko-2021-debiasing-pre-trained-contextualised-embeddings/)
110. [**Understanding Undesirable Word Embedding Associations**Kawin Ethayarajh, David Duvenaud, Graeme Hirst*2019 · 171 citations*](https://md.research.elamur.ai/free/en/papers/ethayarajh-2019-understanding-undesirable-word-embedding-associations/)
111. [**Low-Resource Languages Jailbreak GPT-4**Zheng-Xin Yong, Cristina Menghini, Stephen H. Bach*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/yong-2023-low-resource-languages-jailbreak-gpt/)
112. [**The Capacity for Moral Self-Correction in Large Language Models**Deep Ganguli, Amanda Askell, Nicholas Schiefer, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/ganguli-2023-capacity-moral-self-correction-large/)
113. [**On Releasing Annotator-Level Labels and Information in Datasets**Vinodkumar Prabhakaran, Aida Mostafazadeh Davani, Mark Diaz*2021 · 168 citations*](https://md.research.elamur.ai/free/en/papers/prabhakaran-2021-releasing-annotator-level-labels-information/)
114. [**Social Bias in Elicited Natural Language Inferences**Rachel Rudinger, Chandler May, Benjamin Van Durme*2017 · 168 citations*](https://md.research.elamur.ai/free/en/papers/rudinger-2017-social-bias-elicited-natural-language/)
115. [**Towards Controllable Biases in Language Generation**Emily Sheng, Kai-Wei Chang, Prem Natarajan, et al.*2020 · 167 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2020-controllable-biases-language-generation/)
116. [**Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4**Kent K. Chang, Mackenzie Cramer, Sandeep Soni, et al.*2023 · 166 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-speak-memory-archaeology-books-known/)
117. [**“I'm sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset**Eric Michael Smith, Melissa Hall, Melanie Kambadur, et al.*2022 · 165 citations*](https://md.research.elamur.ai/free/en/papers/smith-2022-i-m-sorry-hear-that/)
118. [**Sustainable Modular Debiasing of Language Models**Anne Lauscher, Tobias Lueken, Goran Glavaš*2021 · 165 citations*](https://md.research.elamur.ai/free/en/papers/lauscher-2021-sustainable-modular-debiasing-language-models/)
119. [**Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models**Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.*2023 · 164 citations*](https://md.research.elamur.ai/free/en/papers/ahia-2023-do-all-languages-cost-same/)
120. [**Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis**Saif M. Mohammad*2022 · 164 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2022-ethics-sheet-automatic-emotion-recognition/)
121. [**Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation**Eva Vanmassenhove, Dimitar Shterionov, Matthew Gwilliam*2021 · 161 citations*](https://md.research.elamur.ai/free/en/papers/vanmassenhove-2021-machine-translationese-effects-algorithmic-bias/)
122. [**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/)
123. [**WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs**Seungju Han, Kavel Rao, Allyson Ettinger, et al.*2024 · 155 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-wildguard-open-one-stop-moderation/)
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125. [**Generating Counter Narratives against Online Hate Speech: Data and Strategies**Serra Sinem Tekiroğlu, Yi-Ling Chung, Marco Guerini*2020 · 153 citations*](https://md.research.elamur.ai/free/en/papers/tekiroglu-2020-generating-counter-narratives-against-online/)

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