---
title: "Social Sciences — papers, page 2 — Elamur Research"
description: "Social Sciences: 744 papers of the Elamur Research library · page 2 of 9 · 26–125, the most cited first — our summary of each, its ideas…"
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# 🤝 Social Sciences

744 papers · page 2 of 9 · 26–125 · 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/) [1](https://md.research.elamur.ai/free/en/shelves/social-sciences/) **2** [3](https://md.research.elamur.ai/free/en/shelves/social-sciences/page/3/) … [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/3/)

26. [**Language (Technology) is Power: A Critical Survey of “Bias” in NLP**Su Lin Blodgett, Solon Barocas, Hal Daumé III, et al.*2020 · 1,703 citations*](https://md.research.elamur.ai/free/en/papers/blodgett-2020-language-technology-is-power-critical/)
27. [**ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks**Fabrizio Gilardi, Meysam Alizadeh, Maël Kubli*2023 · 1,533 citations*](https://md.research.elamur.ai/free/en/papers/gilardi-2023-chatgpt-outperforms-crowd-workers-text/)
28. [**LEGAL-BERT: The Muppets straight out of Law School**Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, et al.*2020 · 1,493 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2020-legal-bert-muppets-straight-out/)
29. [**NLTK: The Natural Language Toolkit**Steven Bird*2006 · 1,374 citations*](https://md.research.elamur.ai/free/en/papers/bird-2006-nltk-natural-language-toolkit/)
30. [**Optimizing Semantic Coherence in Topic Models**David Mimno, Hanna Wallach, Edmund Talley, et al.*2011 · 1,338 citations*](https://md.research.elamur.ai/free/en/papers/mimno-2011-optimizing-semantic-coherence-topic-models/)
31. [**SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter**Valerio Basile, Cristina Bosco, Elisabetta Fersini, et al.*2019 · 1,312 citations*](https://md.research.elamur.ai/free/en/papers/basile-2019-semeval-2019-task-5-multilingual/)
32. [**RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models**Samuel Gehman, Suchin Gururangan, Maarten Sap, et al.*2020 · 1,281 citations*](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/)
33. [**Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change**William L. Hamilton, Jure Leskovec, Dan Jurafsky*2016 · 1,273 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2016-diachronic-word-embeddings-reveal-statistical/)
34. [**Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2018 · 1,186 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2018-gender-bias-coreference-resolution-evaluation/)
35. [**The Risk of Racial Bias in Hate Speech Detection**Maarten Sap, Dallas Card, Saadia Gabriel, et al.*2019 · 1,152 citations*](https://md.research.elamur.ai/free/en/papers/sap-2019-risk-racial-bias-hate-speech/)
36. [**StereoSet: Measuring stereotypical bias in pretrained language models**Moin Nadeem, Anna Bethke, Siva Reddy*2021 · 1,132 citations*](https://md.research.elamur.ai/free/en/papers/nadeem-2021-stereoset-measuring-stereotypical-bias-pretrained/)
37. [**SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)**Marcos Zampieri, Shervin Malmasi, Preslav Nakov, et al.*2019 · 1,113 citations*](https://md.research.elamur.ai/free/en/papers/zampieri-2019-semeval-2019-task-6-identifying/)
38. [**Bias and Fairness in Large Language Models: A Survey**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,074 citations*](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/)
39. [**Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking**Hannah Rashkin, Eunsol Choi, Jin Yea Jang, et al.*2017 · 1,074 citations*](https://md.research.elamur.ai/free/en/papers/rashkin-2017-truth-varying-shades-analyzing-language/)
40. [**OpenAI o1 System Card**OpenAI, Aaron Jaech, Adam Kalai, et al.*2024 · 1,065 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-openai-o1-system-card/)
41. [**Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2017 · 1,032 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2017-men-also-like-shopping-reducing/)
42. [**Automated Hate Speech Detection and the Problem of Offensive Language**Thomas Davidson, Dana Warmsley, Michael Macy, et al.*2017 · 1,015 citations*](https://md.research.elamur.ai/free/en/papers/davidson-2017-automated-hate-speech-detection-problem/)
43. [**Out of One, Many: Using Language Models to Simulate Human Samples**Lisa P. Argyle, Ethan C. Busby, Nancy Fulda, et al.*2022 · 944 citations*](https://md.research.elamur.ai/free/en/papers/argyle-2022-out-one-many-using-language/)
44. [**Topic Modeling in Embedding Spaces**Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei*2020 · 915 citations*](https://md.research.elamur.ai/free/en/papers/dieng-2020-topic-modeling-embedding-spaces/)
45. [**CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models**Nikita Nangia, Clara Vania, Rasika Bhalerao, et al.*2020 · 887 citations*](https://md.research.elamur.ai/free/en/papers/nangia-2020-crows-pairs-challenge-dataset-measuring/)
46. [**Gender Bias in Coreference Resolution**Rachel Rudinger, Jason Naradowsky, Brian Leonard, et al.*2018 · 852 citations*](https://md.research.elamur.ai/free/en/papers/rudinger-2018-gender-bias-coreference-resolution/)
47. [**Automatic Evaluation of Topic Coherence**David Newman, Jey Han Lau, Karl Grieser, et al.*2010 · 825 citations*](https://md.research.elamur.ai/free/en/papers/newman-2010-automatic-evaluation-topic-coherence/)
48. [**A Stylometric Inquiry into Hyperpartisan and Fake News**Martin Potthast, Johannes Kiesel, Kevin Reinartz, et al.*2018 · 810 citations*](https://md.research.elamur.ai/free/en/papers/potthast-2018-stylometric-inquiry-into-hyperpartisan-fake/)
49. [**Finding Deceptive Opinion Spam by Any Stretch of the Imagination**Myle Ott, Yejin Choi, Claire Cardie, et al.*2011 · 810 citations*](https://md.research.elamur.ai/free/en/papers/ott-2011-finding-deceptive-opinion-spam-any/)
50. [**Can Large Language Models Transform Computational Social Science?**Caleb Ziems, William Held, Omar Shaikh, et al.*2023 · 777 citations*](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/)
51. [**Rumor has it: Identifying Misinformation in Microblogs**Vahed Qazvinian, Emily Rosengren, Dragomir R. Radev, et al.*2011 · 771 citations*](https://md.research.elamur.ai/free/en/papers/qazvinian-2011-rumor-has-it-identifying-misinformation/)
52. [**Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter**Zeerak Waseem*2016 · 767 citations*](https://md.research.elamur.ai/free/en/papers/waseem-2016-are-you-racist-or-am/)
53. [**On Measuring Social Biases in Sentence Encoders**Chandler May, Alex Wang, Shikha Bordia, et al.*2019 · 761 citations*](https://md.research.elamur.ai/free/en/papers/may-2019-measuring-social-biases-sentence-encoders/)
54. [**The Woman Worked as a Babysitter: On Biases in Language Generation**Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, et al.*2019 · 753 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2019-woman-worked-as-babysitter-biases/)
55. [**A Joint Model of Text and Aspect Ratings for Sentiment Summarization**Ivan Titov, Ryan McDonald*2008 · 729 citations*](https://md.research.elamur.ai/free/en/papers/titov-2008-joint-model-text-aspect-ratings/)
56. [**Mitigating Gender Bias in Natural Language Processing: Literature Review**Tony Sun, Andrew Gaut, Shirlyn Tang, et al.*2019 · 718 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-mitigating-gender-bias-natural-language/)
57. [**Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning**Jing Ma, Wei Gao, Kam-Fai Wong*2017 · 714 citations*](https://md.research.elamur.ai/free/en/papers/ma-2017-detect-rumors-microblog-posts-propagation/)
58. [**Rumor Detection on Twitter with Tree-structured Recursive Neural Networks**Jing Ma, Wei Gao, Kam-Fai Wong*2018 · 708 citations*](https://md.research.elamur.ai/free/en/papers/ma-2018-rumor-detection-twitter-tree-structured/)
59. [**Fact or Fiction: Verifying Scientific Claims**David Wadden, Shanchuan Lin, Kyle Lo, et al.*2020 · 706 citations*](https://md.research.elamur.ai/free/en/papers/wadden-2020-fact-or-fiction-verifying-scientific/)
60. [**Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality**Jey Han Lau, David Newman, Timothy Baldwin*2014 · 702 citations*](https://md.research.elamur.ai/free/en/papers/lau-2014-machine-reading-tea-leaves-automatically/)
61. [**Understanding Abuse: A Typology of Abusive Language Detection Subtasks**Zeerak Waseem, Thomas Davidson, Dana Warmsley, et al.*2017 · 697 citations*](https://md.research.elamur.ai/free/en/papers/waseem-2017-understanding-abuse-typology-abusive-language/)
62. [**CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society**Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, et al.*2023 · 692 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-camel-communicative-agents-mind/)
63. [**Social Bias Frames: Reasoning about Social and Power Implications of Language**Maarten Sap, Saadia Gabriel, Lianhui Qin, et al.*2020 · 681 citations*](https://md.research.elamur.ai/free/en/papers/sap-2020-social-bias-frames-reasoning-about/)
64. [**The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks**Nicholas Carlini, Chang Liu, Ulfar Erlingsson, et al.*2018 · 668 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2018-secret-sharer-evaluating-testing-unintended/)
65. [**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/)
66. [**CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data**Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, et al.*2020 · 651 citations*](https://md.research.elamur.ai/free/en/papers/wenzek-2020-ccnet-extracting-high-quality-monolingual/)
67. [**HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models**Junyi Li, Xiaoxue Cheng, Wayne Xin Zhao, et al.*2023 · 647 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-halueval-large-scale-hallucination-evaluation/)
68. [**A Survey on Automated Fact-Checking**Zhijiang Guo, Michael Schlichtkrull, Andreas Vlachos*2022 · 646 citations*](https://md.research.elamur.ai/free/en/papers/guo-2022-survey-automated-fact-checking/)
69. [**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/)
70. [**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/)
71. [**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/)
72. [**Hate Speech Dataset from a White Supremacy Forum**Ona de Gibert, Naiara Perez, Aitor García-Pablos, et al.*2018 · 583 citations*](https://md.research.elamur.ai/free/en/papers/gibert-2018-hate-speech-dataset-white-supremacy/)
73. [**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/)
74. [**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/)
75. [**ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection**Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, et al.*2022 · 571 citations*](https://md.research.elamur.ai/free/en/papers/hartvigsen-2022-toxigen-large-scale-machine-generated/)
76. [**An Unsupervised Aspect-Sentiment Model for Online Reviews**Samuel Brody, Noemie Elhadad*2010 · 570 citations*](https://md.research.elamur.ai/free/en/papers/brody-2010-unsupervised-aspect-sentiment-model-online/)
77. [**Development and Use of a Gold-Standard Data Set for Subjectivity Classifications**Janyce M. Wiebe, Rebecca F. Bruce, Thomas P. O'Hara*1999 · 568 citations*](https://md.research.elamur.ai/free/en/papers/wiebe-1999-development-use-gold-standard-data/)
78. [**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/)
79. [**Neural Legal Judgment Prediction in English**Ilias Chalkidis, Ion Androutsopoulos, Nikolaos Aletras*2019 · 558 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2019-neural-legal-judgment-prediction-english/)
80. [**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/)
81. [**MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models**Chaoyou Fu, Peixian Chen, Yunhang Shen, et al.*2023 · 529 citations*](https://md.research.elamur.ai/free/en/papers/fu-2023-mme-comprehensive-evaluation-benchmark-multimodal/)
82. [**Fact Checking: Task definition and dataset construction**Andreas Vlachos, Sebastian Riedel*2014 · 523 citations*](https://md.research.elamur.ai/free/en/papers/vlachos-2014-fact-checking-task-definition-dataset/)
83. [**How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence**Haoxi Zhong, Chaojun Xiao, Cunchao Tu, et al.*2020 · 518 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2020-how-does-nlp-benefit-legal/)
84. [**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/)
85. [**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/)
86. [**TabFact: A Large-scale Dataset for Table-based Fact Verification**Wenhu Chen, Hongmin Wang, Jianshu Chen, et al.*2019 · 508 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-tabfact-large-scale-dataset-table/)
87. [**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/)
88. [**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/)
89. [**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/)
90. [**Legal Judgment Prediction via Topological Learning**Haoxi Zhong, Zhipeng Guo, Cunchao Tu, et al.*2018 · 480 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2018-legal-judgment-prediction-topological-learning/)
91. [**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/)
92. [**An Unsupervised Neural Attention Model for Aspect Extraction**Ruidan He, Wee Sun Lee, Hwee Tou Ng, et al.*2017 · 473 citations*](https://md.research.elamur.ai/free/en/papers/he-2017-unsupervised-neural-attention-model-aspect/)
93. [**Learning Whom to Trust with MACE**Dirk Hovy, Taylor Berg-Kirkpatrick, Ashish Vaswani, et al.*2013 · 472 citations*](https://md.research.elamur.ai/free/en/papers/hovy-2013-learning-whom-trust-mace/)
94. [**GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media**Yi-Ju Lu, Cheng-Te Li*2020 · 468 citations*](https://md.research.elamur.ai/free/en/papers/lu-2020-gcan-graph-aware-co-attention/)
95. [**Defending Against Neural Fake News**Rowan Zellers, Ari Holtzman, Hannah Rashkin, et al.*2019 · 466 citations*](https://md.research.elamur.ai/free/en/papers/zellers-2019-defending-against-neural-fake-news/)
96. [**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/)
97. [**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/)
98. [**Emergent: a novel data-set for stance classification**William Ferreira, Andreas Vlachos*2016 · 458 citations*](https://md.research.elamur.ai/free/en/papers/ferreira-2016-emergent-novel-data-set-stance/)
99. [**Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora**William L. Hamilton, Kevin Clark, Jure Leskovec, et al.*2016 · 434 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2016-inducing-domain-specific-sentiment-lexicons/)
100. [**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/)
101. [**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/)
102. [**SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours**Leon Derczynski, Kalina Bontcheva, Maria Liakata, et al.*2017 · 429 citations*](https://md.research.elamur.ai/free/en/papers/derczynski-2017-semeval-2017-task-8-rumoureval/)
103. [**The Internal State of an LLM Knows When It's Lying**Amos Azaria, Tom Mitchell*2023 · 427 citations*](https://md.research.elamur.ai/free/en/papers/azaria-2023-internal-state-llm-knows-when/)
104. [**Deal or No Deal? End-to-End Learning of Negotiation Dialogues**Mike Lewis, Denis Yarats, Yann Dauphin, et al.*2017 · 421 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2017-deal-or-no-deal-end/)
105. [**Recognizing Stances in Ideological On-Line Debates**Swapna Somasundaran, Janyce Wiebe*2010 · 416 citations*](https://md.research.elamur.ai/free/en/papers/somasundaran-2010-recognizing-stances-ideological-line-debates/)
106. [**Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations**Aida Mostafazadeh Davani, Mark Díaz, Vinodkumar Prabhakaran*2022 · 415 citations*](https://md.research.elamur.ai/free/en/papers/davani-2022-dealing-disagreements-looking-beyond-majority/)
107. [**Abusive Language Detection on Arabic Social Media**Hamdy Mubarak, Kareem Darwish, Walid Magdy*2017 · 414 citations*](https://md.research.elamur.ai/free/en/papers/mubarak-2017-abusive-language-detection-arabic-social/)
108. [**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/)
109. [**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/)
110. [**Multilingual and Multi-Aspect Hate Speech Analysis**Nedjma Ousidhoum, Zizheng Lin, Hongming Zhang, et al.*2019 · 410 citations*](https://md.research.elamur.ai/free/en/papers/ousidhoum-2019-multilingual-multi-aspect-hate-speech/)
111. [**The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale**Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, et al.*2024 · 404 citations*](https://md.research.elamur.ai/free/en/papers/penedo-2024-fineweb-datasets-decanting-web-finest/)
112. [**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/)
113. [**The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes**Douwe Kiela, Hamed Firooz, Aravind Mohan, et al.*2020 · 404 citations*](https://md.research.elamur.ai/free/en/papers/kiela-2020-hateful-memes-challenge-detecting-hate/)
114. [**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/)
115. [**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/)
116. [**DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning**Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, et al.*2018 · 391 citations*](https://md.research.elamur.ai/free/en/papers/popat-2018-declare-debunking-fake-news-false/)
117. [**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/)
118. [**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/)
119. [**Truth Is a Lie: Crowd Truth and the Seven Myths of Human Annotation**Lora M. Aroyo, Chris Welty*2015 · 385 citations*](https://md.research.elamur.ai/free/en/papers/aroyo-2015-truth-lie-crowd-truth-seven/)
120. [**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/)
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