{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:26:48Z","timestamp":1776094008894,"version":"3.50.1"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032059611","type":"print"},{"value":"9783032059628","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Large Language Models (LLMs) are increasingly deployed as gateways to information, yet their content moderation practices remain underexplored. This work investigates the extent to which LLMs refuse to answer or omit information when prompted on political topics. To do so, we distinguish between hard censorship (i.e., generated refusals, error messages, or canned denial responses) and soft censorship (i.e., selective omission or downplaying of key elements), which we identify in LLMs\u2019 responses when asked to provide information on a broad range of political figures. Our analysis covers 14 state-of-the-art models from Western countries, China, and Russia, prompted in all six official United Nations (UN) languages. Our analysis suggests that although censorship is observed across the board, it is predominantly tailored to an LLM provider\u2019s domestic audience and typically manifests as either hard censorship or soft censorship (though rarely both concurrently). These findings underscore the need for ideological and geographic diversity among publicly available LLMs, and greater transparency in LLM moderation strategies to facilitate informed user choices. All data are made freely available.<\/jats:p>","DOI":"10.1007\/978-3-032-05962-8_16","type":"book-chapter","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T19:37:57Z","timestamp":1759520277000},"page":"265-281","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices"],"prefix":"10.1007","author":[{"given":"Sander","family":"Noels","sequence":"first","affiliation":[]},{"given":"Guillaume","family":"Bied","sequence":"additional","affiliation":[]},{"given":"Maarten","family":"Buyl","sequence":"additional","affiliation":[]},{"given":"Alexander","family":"Rogiers","sequence":"additional","affiliation":[]},{"given":"Yousra","family":"Fettach","sequence":"additional","affiliation":[]},{"given":"Jefrey","family":"Lijffijt","sequence":"additional","affiliation":[]},{"given":"Tijl","family":"De Bie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"16_CR1","unstructured":"Ahmed, M., Knockel, J.: The impact of online censorship on LLMs. In: Free and Open Communications on the Internet (2024)"},{"key":"16_CR2","unstructured":"Bai, Y., et\u00a0al.: Constitutional AI: harmlessness from AI feedback. arxiv:2212.08073 (2022)"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Bengio, Y., et\u00a0al.: International AI safety report. arXiv:2501.17805 (2025)","DOI":"10.70777\/si.v2i2.14755"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Buyl, M., et\u00a0al.: Large language models reflect the ideology of their creators. arXiv:2410.18417 (2025)","DOI":"10.1038\/s44387-025-00048-0"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Chun, J., de\u00a0Witt, C.S., Elkins, K.: Comparative global ai regulation: policy perspectives from the eu, china, and the us. arXiv preprint arXiv:2410.21279 (2024)","DOI":"10.2139\/ssrn.5104429"},{"key":"16_CR6","unstructured":"Dong, B., Lee, J.R., Zhu, Z., Srinivasan, B.: Assessing large language models for online extremism research: identification, explanation, and new knowledge. arXiv preprint arXiv:2408.16749 (2024)"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Du, H., Liu, S., Zheng, L., Cao, Y., Nakamura, A., Chen, L.: Privacy in fine-tuning large language models: attacks, defenses, and future directions. arXiv:2412.16504 (2024)","DOI":"10.1007\/978-981-96-8183-9_25"},{"key":"16_CR8","unstructured":"Glukhov, D., Shumailov, I., Gal, Y., Papernot, N., Papyan, V.: LLM censorship: a machine learning challenge or a computer security problem? arXiv:2307.10719 (2023)"},{"key":"16_CR9","unstructured":"Gu, J., et\u00a0al.: A survey on llm-as-a-judge. arXiv preprint arXiv:2411.15594 (2024)"},{"key":"16_CR10","unstructured":"Hadfield, G.K., Clark, J.: Regulatory markets: the future of ai governance. arXiv preprint arXiv:2304.04914 (2023)"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Kumar, D., AbuHashem, Y.A., Durumeric, Z.: Watch your language: investigating content moderation with large language models. 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