{"id":28389,"date":"2026-05-13T22:10:12","date_gmt":"2026-05-13T22:10:12","guid":{"rendered":"https:\/\/www.europesays.com\/news\/28389\/"},"modified":"2026-05-13T22:10:12","modified_gmt":"2026-05-13T22:10:12","slug":"state-media-control-influences-large-language-models","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/news\/28389\/","title":{"rendered":"State media control influences large language models"},"content":{"rendered":"

Palmer, A. & Spirling, A. Large language models can argue in convincing ways about politics, but humans dislike AI authors: implications for governance. Polit. Sci. 75, 281\u2013291 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bai, H. et al. LLM-generated messages can persuade humans on policy issues. Nat. Commun. 16, 6037 (2025).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Hackenburg, K. & Margetts, H. Evaluating the persuasive influence of political microtargeting with large language models. Proc. Natl Acad. Sci. USA 121, e2403116121 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Salvi, F. et al. On the conversational persuasiveness of GPT-4. Nat. Hum. Behav. 9, 1645\u20131653 (2025).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Costello, T. H., Pennycook, G. & Rand, D. G. Durably reducing conspiracy beliefs through dialogues with AI. Science 385, eadq1814 (2024).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Carrasco-Farre, C. Large language models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments. Preprint at https:\/\/arxiv.org\/abs\/2404.09329<\/a> (2024).<\/p>\n

Tessler, M. H. et al. AI can help humans find common ground in democratic deliberation. Science 386, eadq2852 (2024).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Goldstein, J. A. et al. How persuasive is AI-generated propaganda? PNAS Nexus 3, pgae034 (2024).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Fisher, J. et al. Biased LLMs can influence political decision-making. In Proc. 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (eds Che, W. et al.) 6559\u20136607 (Association for Computational Linguistics, 2025).<\/p>\n

Saenger, T.R. et al. AutoPersuade: a framework for evaluating and explaining persuasive arguments. In Proc. 2024 Conference on Empirical Methods in Natural Language Processing (eds Al-Onaizan, Y., Bansal, M. & Chen, Y.-N.) 16325\u201316342 (Association for Computational Linguistics, 2024).<\/p>\n

Islas-Carmona, J. O., Guti\u00e9rrez-Cort\u00e9s, F. I. & Arribas-Urrutia, A. Disinformation and political propaganda: an exploration of the risks of artificial intelligence. Explor. Media Ecol. 23, 105\u2013120 (2024).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Woolley, S. Manufacturing Consensus: Understanding Propaganda in the Era of Automation and Anonymity (Yale Univ. Press, 2023).<\/p>\n

Broockman, D. & Kalla, J. Durably reducing transphobia: a field experiment on door-to-door canvassing. Science 352, 220\u2013224 (2016).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Roghanizad, M. M. & Bohns, V. K. Ask in person: you\u2019re less persuasive than you think over email. J. Exp. Soc. Psychol. 69, 223\u2013226 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Buyl, M. et al. Large language models reflect the ideology of their creators. npj Artif. Intell. 2, 7 (2026).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Guey, W. et al. Mapping geopolitical bias in 11 large language models: a bilingual, dual-framing analysis of US-China tensions. Preprint at https:\/\/arxiv.org\/abs\/2503.23688<\/a> (2025).<\/p>\n

McCarthy, S. DeepSeek is giving the world a window into Chinese censorship and information control. CNN https:\/\/edition.cnn.com\/2025\/01\/29\/china\/deepseek-ai-china-censorship-moderation-intl-hnk<\/a> (29 January 2025).<\/p>\n

Ouyang, Y., Nellis, S. and Tong, Q. DeepSeek hit by cyberattack as users flock to Chinese AI startup. Reuters https:\/\/www.reuters.com\/technology\/artificial-intelligence\/chinese-ai-startup-deepseek-overtakes-chatgpt-apple-app-store-2025-01-27\/<\/a> (27 January 2025).<\/p>\n

Kachwala, Z. Musk\u2019s xAI updates Grok chatbot after \u2018white genocide\u2019 comments. Reuters https:\/\/www.reuters.com\/business\/musks-xai-updates-grok-chatbot-after-white-genocide-comments-2025-05-17\/<\/a> (17 May 2025).<\/p>\n

O\u2019Brien, M. Google says its AI image-generator would sometimes \u2018overcompensate\u2019 for diversity. Associated Press https:\/\/apnews.com\/article\/google-gemini-ai-chatbot-imagegenerator-race-c7e14de837aa65dd84f6e7ed6cfc4f4b<\/a> (23 February 2024).<\/p>\n

O\u2019Brien, M. Elon Musk\u2019s AI company says Grok chatbot focus on South Africa\u2019s racial politics was \u2018unauthorized\u2019. Associated Press https:\/\/apnews.com\/article\/grok-ai-south-africa-64ce5f240061ca0b88d5af4c424e1f3b<\/a> (16 May 2025).<\/p>\n

Price, M. E. Media and Sovereignty: The Global Information Revolution and its Challenge to State Power (MIT Press, 2002).<\/p>\n

Hallin, D. C. & Mancini, P. Comparing Media Systems: Three Models of Media and Politics (Cambridge Univ. Press, 2004).<\/p>\n

Gururangan, S. et al. Don\u2019t stop pretraining: adapt language models to domains and tasks. In Proc. 58th Annual Meeting of the Association for Computational Linguistics (eds Jurafsky, D. et al.) 8342\u20138360 (Association for Computational Linguistics, 2020).<\/p>\n

Bender, E. M. et al. On the dangers of stochastic parrots: can language models be too big? In Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency 610\u2013623 (Association for Computing Machinery, 2021).<\/p>\n

Kreutzer, J. et al. Quality at a glance: an audit of web-crawled multilingual datasets. Trans. Assoc. Comput. Linguist. 10, 50\u201372 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Blodgett, S. L. et al. Language (technology) is power: a critical survey of \u201cbias\u201d in NLP. In Proc. Annual Meeting of the Association for Computational Linguistics (eds Jurafsky, D. et al.) 5454\u20135476 (Association for Computational Linguistics, 2020).<\/p>\n

Ouyang, L. et al. Training language models to follow instructions with human feedback. Adv. Neural Inf. Process. Syst. 35, 27730\u201327744 (2022).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Bai, Y. et al. Constitutional AI: harmlessness from AI feedback. Preprint at https:\/\/arxiv.org\/abs\/2212.08073<\/a> (2022).<\/p>\n

Bult\u00e9, B. & Terryn, A. R. LLMs and cultural values: the impact of prompt language and explicit cultural framing. Comput. Linguist. https:\/\/doi.org\/10.1162\/COLI.a.583<\/a> (2026).<\/p>\n

Lu, J. G., Song, L. L. & Zhang, L. D. Cultural tendencies in generative AI. Nat. Hum. Behav. 9, 2360\u20132369 (2025).<\/p>\n

Kay, M., Matuszek, C. & Munson, S. A. Unequal representation and gender stereotypes in image search results for occupations. In Proc. 33rd Annual ACM Conference on Human Factors in Computing Systems (eds Begole, B. et al.) 3819\u20133828 (Association for Computing Machinery, 2015).<\/p>\n

Noble, S. U. Algorithms of Oppression: How Search Engines Reinforce Racism (New York Univ. Press, 2018).<\/p>\n

Broussard, M. More than a Glitch: Confronting Race, Gender, and Ability Bias in Tech (MIT Press, 2023).<\/p>\n

Benjamin, R. Race after Technology: Abolitionist Tools for the New Jim Code (John Wiley & Sons, 2019).<\/p>\n

Buolamwini, J. & Gebru, T. Gender shades: intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency (eds Friedler, S. A. & Wilson, C.) 77\u201391 (PMLR, 2018).<\/p>\n

Barocas, S. & Selbst, A. D. Big data\u2019s disparate impact. Calif. L. Rev. 104, 671 (2016).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Sheng, E. et al. The woman worked as a babysitter: on biases in language generation. In Proc. 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (eds Inui, K. et al.) 3407\u20133412 (Association for Computational Linguistics, 2019).<\/p>\n

Field, A. et al. A survey of race, racism, and anti-racism in NLP. In Proc. 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (eds Zong, C. et al.) 1905\u20131925 (Association for Computational Linguistics, 2021).<\/p>\n

Metaxa, D. et al. An image of society: gender and racial representation and impact in image search results for occupations. Proc. ACM Hum. Comp. Interact. 5, 1\u201323 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Kotek, H., Dockum, R. & Sun, D. Gender bias and stereotypes in large language models. In Proc. ACM Collective Intelligence Conference (eds Bernstein, M. S. et al.) 12\u201324 (Association for Computing Machinery, 2023).<\/p>\n

Omiye, J. A. et al. Large language models propagate race-based medicine. NPJ Digit. Med. 6, 195 (2023).<\/p>\n

Article<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Jowett, G. S. & O\u2019Donnell, V. Propaganda & Persuasion (Sage, 2018).<\/p>\n

Peisakhin, L. & Rozenas, A. Electoral effects of biased media: Russian television in Ukraine. Am. J. Polit. Sci. 62, 535\u2013550 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Selb, P. & Munzert, S. Examining a most likely case for strong campaign effects: Hitler\u2019s speeches and the rise of the Nazi party, 1927-1933. Am. Polit. Sci. Rev. 112, 1050\u20131066 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Rozenas, A. & Stukal, D. How autocrats manipulate economic news: evidence from Russia\u2019s state-controlled television. J. Polit. 81, 982\u2013996 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Huang, H. Propaganda as signaling. Comp. Polit. 47, 419\u2013444 (2015).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Voigtl\u00e4nder, N. & Voth, H.-J. Nazi indoctrination and anti-Semitic beliefs in Germany. Proc. Natl Acad. Sci. USA 112, 7931\u20137936 (2015).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

King, G., Pan, J. & Roberts, M. E. How the Chinese government fabricates social media posts for strategic distraction, not engaged argument. Am. Polit. Sci. Rev. 111, 484\u2013501 (2017).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Stukal, D. et al. Why botter: how pro-government bots fight opposition in Russia. Am. Polit. Sci. Rev. 116, 843\u2013857 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Farzam, A. et al. Opinion manipulation on Farsi Twitter. Sci. Rep. 13, 333 (2023).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Waight, H. et al. The decade-long growth of government-authored news media in China under Xi Jinping. Proc. Natl Acad. Sci. USA 122, e2408260122 (2025).<\/p>\n

Article<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Yang, E. & Roberts, M. E. Censorship of online encyclopedias: implications for NLP models. In Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency 537\u2013548 (Association for Computing Machinery, 2021).<\/p>\n

Zhou, D. & Zhang, Y. Political biases and inconsistencies in bilingual GPT models \u2014 the cases of the US and China. Sci. Rep. 14, 25048 (2024).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Ahmed, M. & Knockel, J. Extended abstract: the impact of online censorship on LLMs. Free and Open Communications on the Internet https:\/\/www.petsymposium.org\/foci\/2024\/foci-2024-0006.pdf<\/a> (2024).<\/p>\n

Urman, A. & Makhortykh, M. The silence of the LLMs: cross-lingual analysis of guardrail-related political bias and false information prevalence in ChatGPT, Google Bard (Gemini), and Bing Chat. Telemat. Informat. 96, 102211 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Spirling, A. & Stewart, B. M. What good is a regression? Inference to the best explanation and the practice of political science research. J. Polit. 87, 1587\u20131599 (2025).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Reporters Without Borders. World Press Freedom Index https:\/\/rsf.org\/en\/index<\/a> (2024).<\/p>\n

Shambaugh, D. in Critical Readings on the Communist Party of China (4 Vols. Set) (ed. Br\u00f8dsgaard, K. E.) 713\u2013751 (Brill, 2017).<\/p>\n

Brady, A.M. Marketing Dictatorship: Propaganda and Thought Work in Contemporary China (Rowman & Littlefield, 2009).<\/p>\n

Stockmann, D. Media Commercialization and Authoritarian Rule in China (Cambridge Univ. Press, 2013).<\/p>\n

Liang, F., Chen, Y. & Zhao, F. The platformization of propaganda: how Xuexi Qiangguo expands persuasion and assesses citizens in China. Int. J. Commun. 15, 20 (2021).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Lu, Y. & Pan, J. Capturing clicks: how the Chinese government uses clickbait to compete for visibility. Polit. Commun. 38, 23\u201354 (2021).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Repnikova, M. & Fang, K. Digital media experiments in China: \u2018revolutionizing\u2019 persuasion under Xi Jinping. China Q. 239, 679\u2013701 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Esarey, A. Winning hearts and minds? Cadres as microbloggers in China. J. Curr. Chinese Aff. 44, 69\u2013103 (2015).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Qin, B., Str\u00f6mberg, D. & Wu, Y. Media bias in China. Am. Econom. Rev. 108, 2442\u20132476 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Pan, J., Shao, Z. & Xu, Y. How government-controlled media shifts policy attitudes through framing. Polit. Sci. Res. Methods 10, 317\u2013332 (2022).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhang, Z. et al. Unveiling linguistic regions in large language models. In Proc. 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (eds Ku, L.-W., Martins, A. & Srikumar, V.) 6228\u20136247 (Association for Computational Linguistics, 2024).<\/p>\n

Qi, J., Fern\u00e1ndez, R. & Bisazza, A. Cross-lingual consistency of factual knowledge in multilingual language models. In Proc. 2023 Conference on Empirical Methods in Natural Language Processing (eds Bouamor, H., Pino, J. & Bali, K.) 10650\u201310666 (Association for Computational Linguistics, 2023).<\/p>\n

Li, B., Haider, S. & Callison-Burch, C. This land is your, my land: evaluating geopolitical bias in language models through territorial disputes. In Proc. 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (eds Duh, K., Gomez, H. & Bethard, S.) 3855\u20133871 (Association for Computational Linguistics, 2024).<\/p>\n

Wendler, C. et al. Do llamas work in English? On the latent language of multilingual transformers. In Proc. 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (eds Ku, L.-W., Martins, A. & Srikumar, V.) 15366\u201315394 (Association for Computational Linguistics, 2024).<\/p>\n

Durmus, E. et al. Towards measuring the representation of subjective global opinions in language models. In 1st Conference on Language Modeling https:\/\/openreview.net\/pdf?id=zl16jLb91v<\/a> (COLM, 2024).<\/p>\n

Shayegani, E. et al. Survey of vulnerabilities in large language models revealed by adversarial attacks. Preprint at https:\/\/arxiv.org\/abs\/2310.10844<\/a> (2023).<\/p>\n

Roberts, M. Censored: Distraction and Diversion Inside China\u2019s Great Firewall (Princeton Univ. Press, 2018).<\/p>\n

Ishihara, S. & Takahashi, H. Quantifying memorization and detecting training data of pre-trained language models using Japanese newspaper. In Proc. 17th International Natural Language Generation Conference (eds Mahamood, S., Le Minh, N. & Ippolito, D.) 165\u2013179 (Association for Computational Linguistics, 2024).<\/p>\n

Fulay, S. et al. On the relationship between truth and political bias in language models. In Proc. 2024 Conference on Empirical Methods in Natural Language Processing (eds Al-Onaizan, Y., Bansal, M. & Chen, Y.-N.) 9004\u20139018 (Association for Computational Linguistics, 2024).<\/p>\n

Nguyen, T. et al. CulturaX: a cleaned, enormous, and multilingual dataset for large language models in 167 languages. In Proc. 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (eds Calzolari, N. et al.) 4226\u20134237 (ELRA and ICCL, 2024).<\/p>\n

Truex, R. Focal points, dissident calendars, and preemptive repression. J. Confl. Resolut. 63, 1032\u20131052 (2019).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Carter, E. B. & Carter, B. L. When autocrats threaten citizens with violence: evidence from China. Br. J. Polit. Sci. 52, 671\u2013696 (2022).<\/p>\n

Schlessinger, J. et al. Exposing the obscured influence of state-controlled media via causal inference of quotation propagation. Sci. Rep. 15, 1110 (2025).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Zhao, W. et al. WildChat: 1M ChatGPT interaction logs in the wild. In 12th International Conference on Learning Representations https:\/\/openreview.net\/forum?id=Bl8u7ZRlbM<\/a> (ICLR, 2024).<\/p>\n

Trussler, M. & Soroka, S. Consumer demand for cynical and negative news frames. Int. J. Press Polit. 19, 360\u2013379 (2014).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Arango-Kure, M., Garz, M. & Rott, A. Bad news sells: the demand for news magazines and the tone of their covers. J. Media Econom. 27, 199\u2013214 (2014).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Christin, A. Counting clicks: quantification and variation in web journalism in the United States and France. Am. J. Sociol. 123, 1382\u20131415 (2018).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

O\u2019Neil, C. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Crown, 2017).<\/p>\n

Fourcade, M. & Healy, K. The Ordinal Society (Harvard Univ. Press, 2024).<\/p>\n

Gillespie, T. Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions that Shape Social Media (Yale Univ. Press, 2018).<\/p>\n

Yang, E. & Roberts, M. E. The authoritarian data problem. J. Democr. 34, 141\u2013150 (2023).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Wang, H. & Sparks, C. Chinese newspaper groups in the digital era: the resurgence of the party press. J. Commun. 69, 94\u2013119 (2019).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 1\u201367 (2020).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Scheible, R. et al. GottBERT: a pure German language model. In Proc. 2024 Conference on Empirical Methods in Natural Language Processing (eds Al-Onaizan, Y., Bansal, M. & Chen, Y.-N.) 21237\u201321250 (Association for Computational Linguistics, 2024).<\/p>\n

Shalumov, V. & Haskey, H. Hero: ROBERTa and longformer Hebrew language models. Preprint at https:\/\/arxiv.org\/abs\/2304.11077<\/a> (2023).<\/p>\n

Serrano, A. V. et al. RigoBERTa: a state-of-the-art language model for Spanish. Preprint at https:\/\/arxiv.org\/abs\/2205.10233<\/a> (2022).<\/p>\n

Shliazhko, O. et al. mGPT: few-shot learners go multilingual. Trans. Assoc. Comput. Linguist. 12, 58\u201379 (2024).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Mandal, P. K. & Mahto, R. An FNet based auto encoder for long sequence news story generation. Preprint at https:\/\/arxiv.org\/abs\/2211.08295<\/a> (2022).<\/p>\n

Boumans, J. et al. The agency makes the (online) news world go round: the impact of news agency content on print and online news. Int. J. Commun. 12, 22 (2018).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Cag\u00e9, J., Herv\u00e9, N. & Viaud, M.-L. The production of information in an online world. Rev. Econom. Stud. 87, 2126\u20132164 (2020).<\/p>\n

Article<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Nicholls, T. Detecting textual reuse in news stories, at scale. Int. J. Commun. 13, 4173\u20134197 (2019).<\/p>\n

Gao, L. et al. The pile: an 800gb dataset of diverse text for language modeling. Preprint at https:\/\/arxiv.org\/abs\/2101.00027<\/a> (2020).<\/p>\n

Carlini, N. et al. Quantifying memorization across neural language models. In 11th International Conference on Learning Representations https:\/\/openreview.net\/forum?id=TatRHT_1cK<\/a> (ICLR, 2023).<\/p>\n

Touvron, H. et al. Llama 2: open foundation and fine-tuned chat models. Preprint at https:\/\/arxiv.org\/abs\/2307.09288<\/a> (2023).<\/p>\n

Chen, L. et al. AlpaGasus: training a better alpaca with fewer data. In International Conference on Learning Representations (eds Kim, B. et al.) https:\/\/proceedings.iclr.cc\/paper_files\/paper\/2024\/hash\/9543942c237ded1b39b1fd37259ff88e-Abstract-Conference.html<\/a> (ICLR, 2024).<\/p>\n

Hu, E. J. et al. LoRA: low-rank adaptation of large language models. In 10th International Conference on Learning Representations https:\/\/openreview.net\/forum?id=nZeVKeeFYf9<\/a> (ICLR, 2022).<\/p>\n

Kirkpatrick, J. et al. Overcoming catastrophic forgetting in neural networks. Proc. Natl Acad. Sci. USA 114, 3521\u20133526 (2017).<\/p>\n

Article<\/a>\u00a0
\n ADS<\/a>\u00a0
\n CAS<\/a>\u00a0
\n PubMed<\/a>\u00a0
\n PubMed Central<\/a>\u00a0
\n
\n Google Scholar<\/a>\u00a0\n <\/p>\n

Leybzon, D. D. & Kervadec, C. Learning, forgetting, remembering: insights from tracking LLM memorization during training. In Proc. 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP (eds Belinkov, Y. et al.) 43\u201357 (Association for Computational Linguistics, 2024).<\/p>\n

Mahomed, Y. et al. Auditing GPT\u2019s content moderation guardrails: can ChatGPT write your favorite TV show? In 2024 ACM Conference on Fairness, Accountability, and Transparency 660\u2013686 (Association for Computing Machinery, 2024).<\/p>\n

Xu, B. NLP Chinese Corpus: large scale Chinese Corpus for NLP (version 1.0). Zenodo https:\/\/doi.org\/10.5281\/zenodo.3402023<\/a> (2019).<\/p>\n

Egami, N. et al. Using imperfect surrogates for downstream inference: design-based supervised learning for social science applications of large language models. Adv. Neural Inf. Process. Syst. 36, 68589\u201368601 (2024).<\/p>\n


\n Google Scholar<\/a>\u00a0\n <\/p>\n

Eberhard, D. M., Simons, G. F. & Fennig, C. D. (eds). Ethnologue: Languages of the World (SIL International, 2024).<\/p>\n","protected":false},"excerpt":{"rendered":"Palmer, A. & Spirling, A. Large language models can argue in convincing ways about politics, but humans dislike…\n","protected":false},"author":2,"featured_media":28390,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[17304,8,1372,1373,9,67,1371,9550,7],"class_list":{"0":"post-28389","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-top-stories","8":"tag-computer-science","9":"tag-headlines","10":"tag-humanities-and-social-sciences","11":"tag-multidisciplinary","12":"tag-news","13":"tag-politics","14":"tag-science","15":"tag-society","16":"tag-top-stories"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@news\/116569559715905335","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/news\/wp-json\/wp\/v2\/posts\/28389","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/news\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/news\/wp-json\/wp\/v2\/comments?post=28389"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/news\/wp-json\/wp\/v2\/posts\/28389\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/news\/wp-json\/wp\/v2\/media\/28390"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/news\/wp-json\/wp\/v2\/media?parent=28389"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/news\/wp-json\/wp\/v2\/categories?post=28389"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/news\/wp-json\/wp\/v2\/tags?post=28389"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}