--- title: "Social Sciences — papers, page 4 — Elamur Research" description: "Social Sciences: 744 papers of the Elamur Research library · page 4 of 9 · 226–325, the most cited first — our summary of each, its ideas…" canonical: "https://research.elamur.ai/free/en/shelves/social-sciences/page/4/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/social-sciences/page/4/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) / page 4 # 🤝 Social Sciences 744 papers · page 4 of 9 · 226–325 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) Society, politics, law, education, communication, geography and the study of language in use. ## Papers (744) [← prev](https://md.research.elamur.ai/free/en/shelves/social-sciences/page/3/) [1](https://md.research.elamur.ai/free/en/shelves/social-sciences/) [2](https://md.research.elamur.ai/free/en/shelves/social-sciences/page/2/) [3](https://md.research.elamur.ai/free/en/shelves/social-sciences/page/3/) **4** [5](https://md.research.elamur.ai/free/en/shelves/social-sciences/page/5/) … [9](https://md.research.elamur.ai/free/en/shelves/social-sciences/page/9/) [next →](https://md.research.elamur.ai/free/en/shelves/social-sciences/page/5/) 226. [**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/) 227. [**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/) 228. [**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/) 229. [**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/) 230. [**“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/) 231. [**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/) 232. [**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/) 233. [**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/) 234. [**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/) 235. [**A Corpus of Turkish Offensive Language on Social Media**Çağrı Çöltekin*2020 · 200 citations*](https://md.research.elamur.ai/free/en/papers/coltekin-2020-corpus-turkish-offensive-language-social/) 236. [**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/) 237. [**Toxicity Detection: Does Context Really Matter?**John Pavlopoulos, Jeffrey Sorensen, Lucas Dixon, et al.*2020 · 198 citations*](https://md.research.elamur.ai/free/en/papers/pavlopoulos-2020-toxicity-detection-does-context-really/) 238. [**Where is Your Evidence: Improving Fact-checking by Justification Modeling**Tariq Alhindi, Savvas Petridis, Smaranda Muresan*2018 · 194 citations*](https://md.research.elamur.ai/free/en/papers/alhindi-2018-where-is-your-evidence-improving/) 239. [**That is a Known Lie: Detecting Previously Fact-Checked Claims**Shaden Shaar, Nikolay Babulkov, Giovanni Da San Martino, et al.*2020 · 193 citations*](https://md.research.elamur.ai/free/en/papers/shaar-2020-that-is-known-lie-detecting/) 240. [**Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies**Anjalie Field, Doron Kliger, Shuly Wintner, et al.*2018 · 193 citations*](https://md.research.elamur.ai/free/en/papers/field-2018-framing-agenda-setting-russian-news/) 241. [**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/) 242. [**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/) 243. [**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/) 244. [**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/) 245. [**A Survey on Stance Detection for Mis- and Disinformation Identification**Momchil Hardalov, Arnav Arora, Preslav Nakov, et al.*2022 · 187 citations*](https://md.research.elamur.ai/free/en/papers/hardalov-2022-survey-stance-detection-mis-disinformation/) 246. [**LawBench: Benchmarking Legal Knowledge of Large Language Models**Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.*2023 · 186 citations*](https://md.research.elamur.ai/free/en/papers/fei-2023-lawbench-benchmarking-legal-knowledge-large/) 247. [**Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text**Shardul Suryawanshi, Bharathi Raja Chakravarthi, Mihael Arcan, et al.*2020 · 186 citations*](https://md.research.elamur.ai/free/en/papers/suryawanshi-2020-multimodal-meme-dataset-multioff-identifying/) 248. [**Integrating Stance Detection and Fact Checking in a Unified Corpus**Ramy Baly, Mitra Mohtarami, James Glass, et al.*2018 · 186 citations*](https://md.research.elamur.ai/free/en/papers/baly-2018-integrating-stance-detection-fact-checking/) 249. [**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/) 250. [**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/) 251. [**Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers**Chenglei Si, Diyi Yang, Tatsunori Hashimoto*2024 · 182 citations*](https://md.research.elamur.ai/free/en/papers/si-2024-llms-generate-novel-research/) 252. [**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/) 253. [**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/) 254. [**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/) 255. [**The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task**Rami Aly, Zhijiang Guo, Michael Sejr Schlichtkrull, et al.*2021 · 178 citations*](https://md.research.elamur.ai/free/en/papers/aly-2021-fact-extraction-verification-over-unstructured/) 256. [**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/) 257. [**On the Risk of Misinformation Pollution with Large Language Models**Yikang Pan, Liangming Pan, Wenhu Chen, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/pan-2023-risk-misinformation-pollution-large-language/) 258. [**ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts**Yuta Koreeda, Christopher Manning*2021 · 177 citations*](https://md.research.elamur.ai/free/en/papers/koreeda-2021-contractnli-dataset-document-level-natural/) 259. [**BillSum: A Corpus for Automatic Summarization of US Legislation**Anastassia Kornilova, Vladimir Eidelman*2019 · 175 citations*](https://md.research.elamur.ai/free/en/papers/kornilova-2019-billsum-corpus-automatic-summarization-us/) 260. [**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/) 261. [**Stance Detection in COVID-19 Tweets**Kyle Glandt, Sarthak Khanal, Yingjie Li, et al.*2021 · 173 citations*](https://md.research.elamur.ai/free/en/papers/glandt-2021-stance-detection-covid-19-tweets/) 262. [**Detecting Harmful Memes and Their Targets**Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee, et al.*2021 · 171 citations*](https://md.research.elamur.ai/free/en/papers/pramanick-2021-detecting-harmful-memes-their-targets/) 263. [**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/) 264. [**Offensive Language Identification in Greek**Zesis Pitenis, Marcos Zampieri, Tharindu Ranasinghe*2020 · 169 citations*](https://md.research.elamur.ai/free/en/papers/pitenis-2020-offensive-language-identification-greek/) 265. [**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/) 266. [**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/) 267. [**COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic**Arkadiy Saakyan, Tuhin Chakrabarty, Smaranda Muresan*2021 · 168 citations*](https://md.research.elamur.ai/free/en/papers/saakyan-2021-covid-fact-fact-extraction-verification/) 268. [**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/) 269. [**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/) 270. [**Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases**Ilias Chalkidis, Manos Fergadiotis, Dimitrios Tsarapatsanis, et al.*2021 · 167 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-paragraph-level-rationale-extraction-through/) 271. [**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/) 272. [**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/) 273. [**Deduplicating Training Data Mitigates Privacy Risks in Language Models**Nikhil Kandpal, Eric Wallace, Colin Raffel*2022 · 166 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-deduplicating-training-data-mitigates-privacy/) 274. [**“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/) 275. [**Arabic Offensive Language on Twitter: Analysis and Experiments**Hamdy Mubarak, Ammar Rashed, Kareem Darwish, et al.*2021 · 165 citations*](https://md.research.elamur.ai/free/en/papers/mubarak-2021-arabic-offensive-language-twitter-analysis/) 276. [**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/) 277. [**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/) 278. [**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/) 279. [**Question Answering for Privacy Policies: Combining Computational and Legal Perspectives**Abhilasha Ravichander, Alan W Black, Shomir Wilson, et al.*2019 · 163 citations*](https://md.research.elamur.ai/free/en/papers/ravichander-2019-question-answering-privacy-policies-combining/) 280. [**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/) 281. [**MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents**Liyan Tang, Philippe Laban, Greg Durrett*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-minicheck-efficient-fact-checking-llms/) 282. [**I Feel Offended, Don't Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language**Tommaso Caselli, Valerio Basile, Jelena Mitrović, et al.*2020 · 159 citations*](https://md.research.elamur.ai/free/en/papers/caselli-2020-i-feel-offended-don-t/) 283. [**FacTool: Factuality Detection in Generative AI — A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios**I-Chun Chern, Steffi Chern, Shiqi Chen, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/chern-2023-factool-factuality-detection-generative-ai/) 284. [**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/) 285. [**CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims**Thomas Diggelmann, Jordan Boyd-Graber, Jannis Bulian, et al.*2020 · 157 citations*](https://md.research.elamur.ai/free/en/papers/diggelmann-2020-climate-fever-dataset-verification-real/) 286. [**Toxic, Hateful, Offensive or Abusive? What Are We Really Classifying? An Empirical Analysis of Hate Speech Datasets**Paula Fortuna, Juan Soler, Leo Wanner*2020 · 157 citations*](https://md.research.elamur.ai/free/en/papers/fortuna-2020-toxic-hateful-offensive-or-abusive/) 287. [**Lawyer LLaMA Technical Report**Quzhe Huang, Mingxu Tao, Chen Zhang, et al.*2023 · 156 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-lawyer-llama-technical-report/) 288. [**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/) 289. [**Probing Pre-Trained Language Models for Cross-Cultural Differences in Values**Arnav Arora, Lucie-aimée Kaffee, Isabelle Augenstein*2023 · 155 citations*](https://md.research.elamur.ai/free/en/papers/arora-2023-probing-pre-trained-language-models/) 290. [**CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering**Nirmalie Wiratunga, Ramitha Abeyratne, Lasal Jayawardena, et al.*2024 · 154 citations*](https://md.research.elamur.ai/free/en/papers/wiratunga-2024-cbr-rag-case-based-reasoning/) 291. [**Can GPT-3 Perform Statutory Reasoning?**Andrew Blair-Stanek, Nils Holzenberger, Benjamin Van Durme*2023 · 153 citations*](https://md.research.elamur.ai/free/en/papers/blair-stanek-2023-gpt-3-perform-statutory-reasoning/) 292. [**Trafilatura: A Web Scraping Library and Command-Line Tool for Text Discovery and Extraction**Adrien Barbaresi*2021 · 153 citations*](https://md.research.elamur.ai/free/en/papers/barbaresi-2021-trafilatura-web-scraping-library-command/) 293. [**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/) 294. [**The First QALB Shared Task on Automatic Text Correction for Arabic**Behrang Mohit, Alla Rozovskaya, Nizar Habash, et al.*2014 · 153 citations*](https://md.research.elamur.ai/free/en/papers/mohit-2014-first-qalb-shared-task-automatic/) 295. [**Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter**Costanza Conforti, Jakob Berndt, Mohammad Taher Pilehvar, et al.*2020 · 151 citations*](https://md.research.elamur.ai/free/en/papers/conforti-2020-will-they-won-t-they/) 296. [**ProsocialDialog: A Prosocial Backbone for Conversational Agents**Hyunwoo Kim, Youngjae Yu, Liwei Jiang, et al.*2022 · 150 citations*](https://md.research.elamur.ai/free/en/papers/kim-2022-prosocialdialog-prosocial-backbone-conversational-agents/) 297. [**A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking**Andreas Hanselowski, Christian Stab, Claudia Schulz, et al.*2019 · 147 citations*](https://md.research.elamur.ai/free/en/papers/hanselowski-2019-richly-annotated-corpus-different-tasks/) 298. [**Fact-Checking Complex Claims with Program-Guided Reasoning**Liangming Pan, Xiaobao Wu, Xinyuan Lu, et al.*2023 · 146 citations*](https://md.research.elamur.ai/free/en/papers/pan-2023-fact-checking-complex-claims-program/) 299. [**Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks**Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, et al.*2022 · 145 citations*](https://md.research.elamur.ai/free/en/papers/mireshghallah-2022-quantifying-privacy-risks-masked-language/) 300. [**UNQOVERing Stereotyping Biases via Underspecified Questions**Tao Li, Daniel Khashabi, Tushar Khot, et al.*2020 · 145 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-unqovering-stereotyping-biases-underspecified-questions/) 301. [**Identifying and Measuring Annotator Bias Based on Annotators' Demographic Characteristics**Hala Al Kuwatly, Maximilian Wich, Georg Groh*2020 · 144 citations*](https://md.research.elamur.ai/free/en/papers/kuwatly-2020-identifying-measuring-annotator-bias-based/) 302. [**Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge**Jiayi Ye, Yanbo Wang, Yue Huang, et al.*2024 · 142 citations*](https://md.research.elamur.ai/free/en/papers/ye-2024-justice-prejudice-quantifying-biases-llm/) 303. [**ConvoKit: A Toolkit for the Analysis of Conversations**Jonathan P. Chang, Caleb Chiam, Liye Fu, et al.*2020 · 137 citations*](https://md.research.elamur.ai/free/en/papers/chang-2020-convokit-toolkit-analysis-conversations/) 304. [**Do LLMs Exhibit Human-like Response Biases? A Case Study in Survey Design**Lindia Tjuatja, Valerie Chen, Tongshuang Wu, et al.*2024 · 136 citations*](https://md.research.elamur.ai/free/en/papers/tjuatja-2024-do-llms-exhibit-human-like/) 305. [**SafetyBench: Evaluating the Safety of Large Language Models**Zhexin Zhang, Leqi Lei, Lindong Wu, et al.*2023 · 136 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-safetybench-evaluating-safety-large/) 306. [**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/) 307. [**Detoxifying Language Models Risks Marginalizing Minority Voices**Albert Xu, Eshaan Pathak, Eric Wallace, et al.*2021 · 135 citations*](https://md.research.elamur.ai/free/en/papers/xu-2021-detoxifying-language-models-risks-marginalizing/) 308. [**Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models**Paul Röttger, Valentin Hofmann, Valentina Pyatkin, et al.*2024 · 134 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2024-political-compass-or-spinning-arrow/) 309. 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