{"slug": "a-google-deepmind-paper-has-multiple-hallucinated-references", "title": "A Google DeepMind paper has multiple hallucinated references", "summary": "A Google DeepMind paper contains multiple hallucinated references, according to a validation report that found fabricated titles and author lists paired with real arXiv and NBER identifiers. The report flags five citations, including one that pairs NBER Working Paper 34255 with a fabricated title and co-author list, and another that misattributes arXiv:2206.01677 to incorrect authors. The findings underscore the need for manual review of AI-generated citations, with validation testing showing an approximately 1% false-positive rate.", "body_md": "You should manually review hallucinations; validation testing shows an approximately 1% false-positive rate.\n**Not Found** is intended for human review: the LLM was unable to determine whether the citation is correct.\n\n| Status | Citation (Found) | Matched Data / Notes | Actions |\n|---|---|---|---|\n|\nHallucination\nEdited\n|\nHow are people using generative ai?A. Chatterji; E. Brynjolfsson; A. Korinek; K. McElheran; R. Seamans; N. Zolas (2025) National Bureau of Economic Research |\nRaw: A. Chatterji, E. Brynjolfsson, A. Korinek, K. McElheran, R. Seamans, and N. Zolas. How are people using generative ai? Technical Report 34255, National Bureau of Economic Research, 2025. URL https://www.nber.org/papers/w34255\nMatch: How People Use ChatGPT\nVenue: National Bureau of Economic Research DOI: 10.3386/w34255 ISBN: URL:\nThe citation is an LLM chimera / hallucination. It pairs the real working paper number (NBER Working Paper 34255 / w34255) and first author Aaron Chatterji with a fabricated/altered title ('How are people using generative ai?' vs real 'How People Use ChatGPT') and an entirely fabricated co-author list (Brynjolfsson, Korinek, McElheran, Seamans, Zolas instead of Cunningham, Deming, Hitzig, Ong, Shan, Wadman).\n|\n|\n|\nHallucination\nEdited\n|\nArgrewrite 2.0: A corpus and model for argumentative text revisionL. Chen; F. Zhang; D. Litman (2022) arXiv |\nRaw: L. Chen, F. Zhang, and D. Litman. Argrewrite 2.0: A corpus and model for argumentative text revision. arXiv preprint arXiv:2206.01677, 2022.\nMatch: ArgRewrite V.2: an Annotated Argumentative Revisions Corpus\nVenue: arXiv DOI: 10.48550/arXiv.2206.01677 ISBN: URL:\nThe citation is an LLM chimera/hallucination: it takes the arXiv identifier arXiv:2206.01677 (which belongs to 'ArgRewrite V.2: an Annotated Argumentative Revisions Corpus' by Kashefi et al.) and conflates it with other frequent ArgRewrite co-authors (Fan Zhang, Diane Litman, Liang Chen) and an altered title ('Argrewrite 2.0: A corpus and model for argumentative text revision').\n|\n|\n|\nHallucination\nEdited\n|\nThe lock-in hypothesis: Stagnation by algorithmB. Hutchinson; S. Saria; O. Etzioni (2025) arXiv |\nRaw: B. Hutchinson, S. Saria, and O. Etzioni. The lock-in hypothesis: Stagnation by algorithm. arXiv preprint arXiv:2506.06166, 2025.\nMatch: The Lock-in Hypothesis: Stagnation by Algorithm\nVenue: arXiv DOI: 10.48550/arXiv.2506.06166 ISBN: URL:\nCrossRef arxiv_static matches title/DOI (score: 1.00), but cited author identities disagree with the official list. Unmatched cited author(s): B. Hutchinson, S. Saria, O. Etzioni. Official authors: Tianyi Alex Qiu, Zhonghao He, Tejasveer Chugh, Max Kleiman-Weiner.\n|\n|\n|\nHallucination\nEdited\n|\nYour brain on chatgpt: Accumulation of cognitive debt when using an ai assistant for essay writing tasksS. Kim; C. Li; D. Alvarez-Melis (2025) arXiv |\nRaw: S. Kim, C. Li, and D. Alvarez-Melis. Your brain on chatgpt: Accumulation of cognitive debt when using an ai assistant for essay writing tasks. arXiv preprint arXiv:2506.08872, 2025.\nMatch: Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task\nVenue: arXiv DOI: 10.48550/arXiv.2506.08872 ISBN: URL:\nCrossRef arxiv_static matches title/DOI (score: 1.00), but cited author identities disagree with the official list. Unmatched cited author(s): S. Kim, C. Li, D. Alvarez-Melis. Official authors: Nataliya Kosmyna, Eugene Hauptmann, Ye Tong Yuan, Jessica Situ, Xian-Hao Liao, Ashly Vivian Beresnitzky, Iris Braunstein, Pattie Maes.\n|\n|\n|\nHallucination\nEdited\n|\nGenerative ai enhances individual creativity but reduces the collective diversity of novel contentW. Wang; S. Ma; S. Yang; J. S. Lee; P. Jain; A. Anderson (2025) Science Advances |\nRaw: W. Wang, S. Ma, S. Yang, J. S. Lee, P. Jain, A. Anderson, et al. Generative ai enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 11(32):eadn5290, 2025.\nMatch: Generative AI enhances individual creativity but reduces the collective diversity of novel content\nVenue: Science Advances DOI: 10.1126/sciadv.adn5290 ISBN: URL:\nThe paper exists in Science Advances (Vol 10, Issue 28, 2024, article eadn5290, DOI: 10.1126/sciadv.adn5290), but the cited authors (W. Wang et al.) are completely fabricated. Under the anti-hallucination rules, pairing a real title/venue with fabricated co-authors is a hallucination / chimera.\n|\n|\n|\nHallucination\nEdited\n|\nThe basic b*** effect: The use of llm-based agents reduces the distinctiveness and diversity of people’s choicesR. Zhou; K. Fiedler (2025) arXiv |\nRaw: R. Zhou and K. Fiedler. The basic b*** effect: The use of llm-based agents reduces the distinctiveness and diversity of people’s choices. arXiv preprint arXiv:2509.02910, 2025.\nMatch: The Basic B*** Effect: The Use of LLM-based Agents Reduces the Distinctiveness and Diversity of People's Choices\nVenue: arXiv DOI: 10.48550/arXiv.2509.02910 ISBN: URL:\nCrossRef arxiv_static matches title/DOI (score: 1.00), but cited author identities disagree with the official list. Unmatched cited author(s): R. Zhou, K. Fiedler. Official authors: Sandra C. Matz, Kimberly Klugescheid, C. Blaine Horton, Sofie Goethals.\n|\n|\n|\nMinor Error\nEdited\n|\nProceedings of the 14th International Conference on Learning RepresentationsNone (2026) |\nRaw: Proceedings of the 14th International Conference on Learning Representations, 2026. URL https://openreview.net/group?id=ICLR.cc/2024/Conference\nThe 14th International Conference on Learning Representations (ICLR) took place in April 2026. However, the citation provides a URL pointing to the 2024 conference (ICLR.cc/2024/Conference, which was the 12th edition) rather than the 2026 conference proceedings URL, representing a minor URL discrepancy.\n|\n|\n|\nMinor Error\nEdited\n|\nLabour mps accused of using AI to write parliamentary speechesR. James (2025) The Independent |\nRaw: R. James. Labour mps accused of using AI to write parliamentary speeches. The Independent, Sep 2025. URL https://www.the-independent.com/news/uk/home-news/labour-mp-ai-chat-gpt-speeches-tugendhat-b2823339.html\nMatch: Labour MPs accused of using ChatGPT to write speeches\nVenue: The Independent DOI: ISBN: URL:\nThe citation references a September 2025 article in The Independent by Rhiannon James at the provided URL. The headline in the citation ('Labour mps accused of using AI to write parliamentary speeches') has minor title wording differences compared to the published headline ('Labour MPs accused of using ChatGPT to write speeches'), representing a minor title discrepancy on the exact same news article.\n|\n|\n|\nMinor Error\nEdited\n|\nPrompt engineering in large language modelsG. Marvin; N. Hellen; D. Jjingo; J. Nakatumba-Nabende (2023) Springer ISBN: 978-9-819-97962-2 |\nRaw: G. Marvin, N. Hellen, D. Jjingo, and J. Nakatumba-Nabende. Prompt engineering in large language models. In International conference on data intelligence and cognitive informatics, pages 387-402. Springer, 2023.\nMatch: Prompt Engineering in Large Language Models\nVenue: International Conference on Data Intelligence and Cognitive Informatics (ICDICI 2023) / Data Intelligence and Cognitive Informatics, Springer DOI: 10.1007/978-981-99-7962-2_30 ISBN: 978-9-819-97962-2 URL:\nThe paper exists in the proceedings of the International Conference on Data Intelligence and Cognitive Informatics (ICDICI 2023), published in the Springer volume 'Data Intelligence and Cognitive Informatics' (part of the Algorithms for Intelligent Systems book series), pp. 387-402, DOI: 10.1007/978-981-99-7962-2_30. The publication year of the book volume is 2024 (conference was 2023) and the first two authors have their given/surnames inverted in the citation.\n|\n|\n|\nVerified\nEdited\n|\nAi suggestions homogenize writing toward western styles and diminish cultural nuancesD. Agarwal; M. Naaman; A. Vashistha (2025) ACM DOI: 10.1145/3706598.3713564 |\nRaw: D. Agarwal, M. Naaman, and A. Vashistha. Ai suggestions homogenize writing toward western styles and diminish cultural nuances. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI ’25, page 1-21. ACM, Apr. 2025. doi: 10.1145/3706598.3713564. URL http://dx.doi.org/10.1145/3706598.3713564\nMatch: AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances\nVenue: Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems DOI: 10.1145/3706598.3713564 ISBN: URL:\nVerified via static CrossRef DOI lookup (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nOpenAI CEO Reports ChatGPT Weekly User Figures at TEDS. Altman (2025) |\nRaw: S. Altman. OpenAI CEO Reports ChatGPT Weekly User Figures at TED. TED Talk / public statement, 2025. Altman stated roughly 10% of the global population uses ChatGPT, equating to 800M-1B weekly users.\nThe citation references Sam Altman's public interview/talk at TED 2025 where he stated that ChatGPT's user base had surged to approximately 800 million weekly active users (roughly 10% of the world population).\n|\n|\n|\nVerified\nEdited\n|\nHomogenization effects of large language models on human creative ideationB. R. Anderson; J. H. Shah; M. Kreminski (2024) ACM DOI: 10.1145/3635636.3656204 |\nRaw: B. R. Anderson, J. H. Shah, and M. Kreminski. Homogenization effects of large language models on human creative ideation. In Creativity and Cognition, CC ’24, page 413-425. ACM, June 2024. doi: 10.1145/3635636.3656204. URL http://dx.doi.org/10.1145/3635636.3656204\nMatch: Homogenization Effects of Large Language Models on Human Creative Ideation\nVenue: Creativity and Cognition DOI: 10.1145/3635636.3656204 ISBN: URL:\nVerified via static CrossRef DOI lookup (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nThe influence of large language models on conversational marketing and communication strategiesP. Borole (2024) Voice of the Publisher |\nRaw: P. Borole. The influence of large language models on conversational marketing and communication strategies. Voice of the Publisher, 10(2):91-99, 2024.\nMatch: The Influence of Large Language Models on Conversational Marketing and Communication Strategies\nVenue: Voice of the Publisher DOI: 10.4236/vp.2024.102008 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nThe development and psychometric properties of liwc-22R. L. Boyd; A. Ashokkumar; S. Seraj; J. W. Pennebaker (2022) University of Texas at Austin |\nRaw: R. L. Boyd, A. Ashokkumar, S. Seraj, and J. W. Pennebaker. The development and psychometric properties of liwc-22. Technical report, University of Texas at Austin, Austin, TX, 2022. URL https://www.liwc.app\nMatch: The development and psychometric properties of LIWC-22\nVenue: University of Texas at Austin DOI: ISBN: URL:\nThe cited work is the official technical report/manual for LIWC-22 published by the University of Texas at Austin in 2022 and available via https://www.liwc.app.\n|\n|\n|\nVerified\nEdited\n|\nAi alignment with changing and influenceable reward functionsM. Carroll; D. Foote; A. Siththaranjan; S. Russell; A. Dragan (2024) arXiv |\nRaw: M. Carroll, D. Foote, A. Siththaranjan, S. Russell, and A. Dragan. Ai alignment with changing and influenceable reward functions. arXiv preprint arXiv:2405.17713, 2024.\nMatch: AI Alignment with Changing and Influenceable Reward Functions\nVenue: arXiv DOI: 10.48550/arXiv.2405.17713 ISBN: URL:\nVerified via arXiv id 2405.17713.\n|\n|\n|\nVerified\nEdited\n|\nPreference dynamics under personalized recommendationsS. Dean; J. Morgenstern (2022) |\nRaw: S. Dean and J. Morgenstern. Preference dynamics under personalized recommendations. In Proceedings of the 23rd ACM Conference on Economics and Computation, pages 795-816, 2022.\nMatch: Preference Dynamics Under Personalized Recommendations\nVenue: Proceedings of the 23rd ACM Conference on Economics and Computation DOI: 10.1145/3490486.3538346 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nEigenwords: spectral word embeddingsP. S. Dhillon; D. P. Foster; L. H. Ungar (2015) J. Mach. Learn. Res. |\nRaw: P. S. Dhillon, D. P. Foster, and L. H. Ungar. Eigenwords: spectral word embeddings. J. Mach. Learn. Res., 16:3035-3078, 2015.\nMatch: Eigenwords: Spectral Word Embeddings\nVenue: Journal of Machine Learning Research DOI: ISBN: URL:\nThe article was published in the Journal of Machine Learning Research (JMLR), Volume 16, Issue 95, pages 3035-3078 in 2015.\n|\n|\n|\nVerified\nEdited\n|\nGenerative artificial intelligence enhances creativity but reduces the diversity of novel contentA. R. Doshi; O. P. Hauser (2023) arXiv |\nRaw: A. R. Doshi and O. P. Hauser. Generative artificial intelligence enhances creativity but reduces the diversity of novel content. arXiv preprint arXiv:2312.00506, 2023.\nMatch: Generative artificial intelligence enhances creativity but reduces the diversity of novel content\nVenue: DOI: 10.2139/ssrn.4535536 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nGenerative ai enhances individual creativity but reduces the collective diversity of novel contentA. R. Doshi; O. P. Hauser (2024) Science Advances DOI: 10.1126/sciadv.adn5290 |\nRaw: A. R. Doshi and O. P. Hauser. Generative ai enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28):eadn5290, 2024. doi: 10.1126/sciadv.adn5290\nMatch: Generative AI enhances individual creativity but reduces the collective diversity of novel content\nVenue: Science Advances DOI: 10.1126/sciadv.adn5290 ISBN: URL:\nVerified via static CrossRef DOI lookup (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nThe Copyeditor’s Handbook: A Guide for Book Publishing and Corporate CommunicationsA. Einsohn; M. Schwartz (2019) University of California Press |\nRaw: A. Einsohn and M. Schwartz. The Copyeditor’s Handbook: A Guide for Book Publishing and Corporate Communications. University of California Press, Berkeley, CA, 4th edition, 2019. See pp. 405-406, “Expository Style,” on the importance of minimal editing.\nMatch: The Copyeditor's Handbook: A Guide for Book Publishing and Corporate Communications\nVenue: DOI: 10.1525/9780520972278 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nPangram predicts 21% of iclr reviews are ai-generatedB. Emi (2025) Pangram Labs Blog |\nRaw: B. Emi. Pangram predicts 21% of iclr reviews are ai-generated. Pangram Labs Blog, Nov 2025. URL https://www.pangram.com/blog/pangram-predicts-21-of-iclr-reviews-are-ai-generated\nMatch: Pangram Predicts 21% of ICLR Reviews are AI-Generated\nVenue: Pangram Labs Blog DOI: ISBN: URL:\nDirect verification from the Pangram Labs website confirmed that Bradley Emi published 'Pangram Predicts 21% of ICLR Reviews are AI-Generated' on November 18, 2025 at the exact cited URL.\n|\n|\n|\nVerified\nEdited\n|\nTechnical report on the pangram ai-generated text classifierB. Emi; M. Spero (2024) arXiv |\nRaw: B. Emi and M. Spero. Technical report on the pangram ai-generated text classifier, 2024. URL https://arxiv.org/abs/2402.14873\nMatch: Technical Report on the Pangram AI-Generated Text Classifier\nVenue: arXiv DOI: 10.48550/arXiv.2402.14873 ISBN: URL:\nVerified via arXiv id 2402.14873.\n|\n|\n|\nVerified\nEdited\n|\nA survey on llm-as-a-judgeJ. Gu; X. Jiang; Z. Shi; H. Tan; X. Zhai; C. Xu; W. Li; Y. Shen; S. Ma; H. Liu; et al. (2024) The Innovation |\nRaw: J. Gu, X. Jiang, Z. Shi, H. Tan, X. Zhai, C. Xu, W. Li, Y. Shen, S. Ma, H. Liu, et al. A survey on llm-as-a-judge. The Innovation, 2024.\nMatch: A Survey on LLM-as-a-Judge\nVenue: arXiv / The Innovation DOI: 10.48550/arXiv.2411.15594 ISBN: URL:\nThe paper 'A Survey on LLM-as-a-Judge' by Jiawei Gu et al. was published in November 2024 (arXiv:2411.15594) and published in The Innovation series.\n|\n|\n|\nVerified\nEdited\n|\nBenchmarking linguistic diversity of large language modelsY. Guo; G. Shang; C. Clavel (2025) Transactions of the Association for Computational Linguistics |\nRaw: Y. Guo, G. Shang, and C. Clavel. Benchmarking linguistic diversity of large language models. Transactions of the Association for Computational Linguistics, 13:1507-1526, 2025.\nMatch: Benchmarking Linguistic Diversity of Large Language Models\nVenue: Transactions of the Association for Computational Linguistics DOI: 10.1162/tacl.a.47 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nAi expands scientists’ impact but contracts science’s focusQ. Hao; F. Xu; Y. Li; J. Evans (2024) arXiv |\nRaw: Q. Hao, F. Xu, Y. Li, and J. Evans. Ai expands scientists’ impact but contracts science’s focus. arXiv e-prints, pages arXiv-2412, 2024.\nMatch: AI Expands Scientists' Impact but Contracts Science's Focus\nVenue: arXiv DOI: 10.48550/arXiv.2412.07727 ISBN: URL:\nThe preprint exists on arXiv with identifier arXiv:2412.07727 (submitted December 2024) by Qianyue Hao, Fengli Xu, Yong Li, and James Evans.\n|\n|\n|\nVerified\nEdited\n|\nThe secret of our success: How culture is driving human evolution, domesticating our species, and making us smarterJ. Henrich (2015) princeton University press |\nRaw: J. Henrich. The secret of our success: How culture is driving human evolution, domesticating our species, and making us smarter. In The secret of our success. princeton University press, 2015.\nMatch: The Secret of Our Success: How Culture Is Driving Human Evolution, Domesticating Our Species, and Making Us Smarter\nVenue: DOI: 10.2307/j.ctvc77f0d ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nArtificial intelligence in communication impacts language and social relationshipsJ. Hohenstein; D. DiFranzo; R. F. Kizilcec; Z. Aghajari; H. Mieczkowski; K. Levy; M. Naaman; J. Hancock; M. F. Jung (2021) arXiv |\nRaw: J. Hohenstein, D. DiFranzo, R. F. Kizilcec, Z. Aghajari, H. Mieczkowski, K. Levy, M. Naaman, J. Hancock, and M. F. Jung. Artificial intelligence in communication impacts language and social relationships. arXiv, 2021. URL https://arxiv.org/abs/2102.05756.\nMatch: Artificial intelligence in communication impacts language and social relationships\nVenue: arXiv DOI: 10.48550/arXiv.2102.05756 ISBN: URL:\nVerified via arXiv id 2102.05756.\n|\n|\n|\nVerified\nEdited\n|\nWay off-policy batch deep reinforcement learning of implicit human preferences in dialogN. Jaques; A. Ghandeharioun; J. H. Shen; C. Ferguson; A. Lapedriza; N. Jones; S. Gu; R. Picard (2019) arXiv |\nRaw: N. Jaques, A. Ghandeharioun, J. H. Shen, C. Ferguson, A. Lapedriza, N. Jones, S. Gu, and R. Picard. Way off-policy batch deep reinforcement learning of implicit human preferences in dialog. arXiv preprint arXiv:1907.00456, 2019.\nMatch: Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog\nVenue: arXiv DOI: 10.48550/arXiv.1907.00456 ISBN: URL:\nVerified via arXiv id 1907.00456.\n|\n|\n|\nVerified\nEdited\n|\nArtificial hivemind: The open-ended homogeneity of language models (and beyond)L. Jiang; Y. Chai; M. Li; M. Liu; R. Fok; N. Dziri; Y. Tsvetkov; M. Sap; A. Albalak; Y. Choi (2025) Neural Information Processing Systems (NeurIPS) |\nRaw: L. Jiang, Y. Chai, M. Li, M. Liu, R. Fok, N. Dziri, Y. Tsvetkov, M. Sap, A. Albalak, and Y. Choi. Artificial hivemind: The open-ended homogeneity of language models (and beyond). Neural Information Processing Systems (NeurIPS), 2025a.\nThe paper exists as a 2025 preprint (arXiv:2510.22954) by the exact specified authors and title. While the citation lists NeurIPS 2025, preprints and conference publications for the same work are treated as the same work under preprint/venue leniency rules.\n|\n|\n|\nVerified\nEdited\n|\nArgrewrite v.2: an annotated argumentative revisions corpusO. Kashefi; T. Afrin; M. Dale; C. Olshefski; A. Godley; D. Litman; R. Hwa (2022) Language Resources and Evaluation DOI: 10.1007/s10579-021-09567-z |\nRaw: O. Kashefi, T. Afrin, M. Dale, C. Olshefski, A. Godley, D. Litman, and R. Hwa. Argrewrite v.2: an annotated argumentative revisions corpus. Language Resources and Evaluation, 56(3):881-915, Jan. 2022. ISSN 1574-0218. doi: 10.1007/s10579-021-09567-z\nMatch: ArgRewrite V.2: an annotated argumentative revisions corpus\nVenue: Language Resources and Evaluation DOI: 10.1007/s10579-021-09567-z ISBN: URL:\nVerified via static CrossRef DOI lookup (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nDigital 2026: More than 1 Billion People Use AIS. Kemp (2025) |\nRaw: S. Kemp. Digital 2026: More than 1 Billion People Use AI. https://datareportal.com/reports/digital-2026-one-billion-people-using-ai\nMatch: Digital 2026: more than 1 billion people use AI\nVenue: DataReportal DOI: ISBN: URL:\nThe web report exists at the exact cited URL (https://datareportal.com/reports/digital-2026-one-billion-people-using-ai) published by Simon Kemp / DataReportal in October 2025 as part of the Digital 2026 series.\n|\n|\n|\nVerified\nEdited\n|\nAlgorithmic extremism: Examining youtube’s rabbit hole of radicalizationM. Ledwich; A. Zaitsev (2019) arXiv |\nRaw: M. Ledwich and A. Zaitsev. Algorithmic extremism: Examining youtube’s rabbit hole of radicalization. arXiv preprint arXiv:1912.11211, 2019.\nMatch: Algorithmic extremism: Examining YouTube's rabbit hole of radicalization\nVenue: First Monday DOI: 10.5210/fm.v25i3.10419 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nMapping the increasing use of llms in scientific papersW. Liang; Y. Zhang; Z. Wu; H. Lepp; W. Ji; X. Zhao; H. Cao; S. Liu; S. He; Z. Huang; et al. (2024) arXiv |\nRaw: W. Liang, Y. Zhang, Z. Wu, H. Lepp, W. Ji, X. Zhao, H. Cao, S. Liu, S. He, Z. Huang, et al. Mapping the increasing use of llms in scientific papers. arXiv preprint arXiv:2404.01268, 2024.\nMatch: Mapping the Increasing Use of LLMs in Scientific Papers\nVenue: arXiv DOI: 10.48550/arXiv.2404.01268 ISBN: URL:\nVerified via arXiv id 2404.01268.\n|\n|\n|\nVerified\nEdited\n|\nQuantifying large language model usage in scientific papersW. Liang; Y. Zhang; Z. Wu; H. Lepp; W. Ji; X. Zhao; H. Cao; S. Liu; S. He; Z. Huang; D. Yang; C. Potts; C. D. Manning; J. Y. Zou (2025) Nature Human Behaviour |\nRaw: W. Liang, Y. Zhang, Z. Wu, H. Lepp, W. Ji, X. Zhao, H. Cao, S. Liu, S. He, Z. Huang, D. Yang, C. Potts, C. D. Manning, and J. Y. Zou. Quantifying large language model usage in scientific papers. Nature Human Behaviour, 9:2599 - 2609, 2025. URL https://api.semanticscholar.org/CorpusID:280523748.\nMatch: Quantifying large language model usage in scientific papers\nVenue: Nature Human Behaviour DOI: 10.1038/s41562-025-02273-8 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nA technique for the measurement of attitudesR. Likert (1932) Archives of Psychology |\nRaw: R. Likert. A technique for the measurement of attitudes. Archives of Psychology, 22(140):1-55, 1932.\nClassic foundational monograph introducing the Likert scale: Rensis Likert (1932), 'A Technique for the Measurement of Attitudes', Archives of Psychology, Vol. 22, No. 140, pp. 1-55.\n|\n|\n|\nVerified\nEdited\n|\nChatgpt decreases idea diversity in brainstormingL. Meincke; G. Nave; C. Terwiesch (2025) Nature human behaviour |\nRaw: L. Meincke, G. Nave, and C. Terwiesch. Chatgpt decreases idea diversity in brainstorming. Nature human behaviour, pages 1-3, 2025.\nMatch: ChatGPT decreases idea diversity in brainstorming\nVenue: Nature Human Behaviour DOI: 10.1038/s41562-025-02173-x ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nThe jensen-shannon divergenceM. L. Menéndez; J. A. Pardo; L. Pardo; M. del Carmen Pardo (1997) Journal of The Franklin Institute-engineering and Applied Mathematics |\nRaw: M. L. Menéndez, J. A. Pardo, L. Pardo, and M. del Carmen Pardo. The jensen-shannon divergence. Journal of The Franklin Institute-engineering and Applied Mathematics, 334:307-318, 1997. URL https://api.semanticscholar.org/CorpusID:120842983.\nMatch: The Jensen-Shannon divergence\nVenue: Journal of the Franklin Institute DOI: 10.1016/s0016-0032(96)00063-4 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nEfficient estimation of word representations in vector spaceT. Mikolov; K. Chen; G. Corrado; J. Dean (2013) arXiv |\nRaw: T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.\nMatch: Efficient Estimation of Word Representations in Vector Space\nVenue: arXiv DOI: 10.48550/arXiv.1301.3781 ISBN: URL:\nVerified via arXiv id 1301.3781.\n|\n|\n|\nVerified\nEdited\n|\nCrowdsourcing a word-emotion association lexiconS. M. Mohammad; P. D. Turney (2013) Computational Intelligence |\nRaw: S. M. Mohammad and P. D. Turney. Crowdsourcing a word-emotion association lexicon. Computational Intelligence, 29(3):436-465, 2013.\nMatch: CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON\nVenue: Computational Intelligence DOI: 10.1111/j.1467-8640.2012.00460.x ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nOne fish, two fish, but not the whole sea: Alignment reduces language models’ conceptual diversityS. K. Murthy; T. Ullman; J. Hu (2025) Association for Computational Linguistics DOI: 10.18653/v1/2025.naacl-long.561 |\nRaw: S. K. Murthy, T. Ullman, and J. Hu. One fish, two fish, but not the whole sea: Alignment reduces language models’ conceptual diversity. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), page 11241-11258. Association for Computational Linguistics, 2025. doi: 10.18653/v1/2025.naacl-long.561\nMatch: One fish, two fish, but not the whole sea: Alignment reduces language models’ conceptual diversity\nVenue: Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) DOI: 10.18653/v1/2025.naacl-long.561 ISBN: URL:\nVerified via static CrossRef DOI lookup (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nAI and the PhD student: friend or foe?L. Nordling (2026) Nature DOI: 10.1038/ d41586-026-00843-y |\nRaw: L. Nordling. AI and the PhD student: friend or foe? Nature, 651:842-844, Mar. 2026. doi: 10.1038/ d41586-026-00843-y\nMatch: AI and the PhD student: friend or foe?\nVenue: Nature DOI: 10.1038/d41586-026-00843-y ISBN: URL:\nVerified via static CrossRef DOI lookup (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nExperimental evidence on the productivity effects of generative artificial intelligenceS. Noy; W. Zhang (2023) Science DOI: 10.1126/science.adh2586 |\nRaw: S. Noy and W. Zhang. Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654):187-192, 2023. doi: 10.1126/science.adh2586\nMatch: Experimental evidence on the productivity effects of generative artificial intelligence\nVenue: Science DOI: 10.1126/science.adh2586 ISBN: URL:\nVerified via static CrossRef DOI lookup (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nChatgpt: Optimizing language models for dialogueOpenAI (2022) OpenAI Blog |\nRaw: OpenAI. Chatgpt: Optimizing language models for dialogue. OpenAI Blog, 2022. URL https://openai.com/blog/chatgpt\nVerified. This is the landmark November 30, 2022 OpenAI announcement blog post introducing ChatGPT, titled 'ChatGPT: Optimizing Language Models for Dialogue' (originally published at https://openai.com/blog/chatgpt, currently mirrored/redirected under openai.com/index/chatgpt/).\n|\n|\n|\nVerified\nEdited\n|\nTraining language models to follow instructions with human feedbackL. Ouyang; J. Wu; X. Jiang; D. Almeida; C. L. Wainwright; P. Mishkin; C. Zhang; S. Agarwal; K. Slama; A. Ray; J. Schulman; J. Hilton; F. Kelton; L. Miller; M. Simens; A. Askell; P. Welinder; P. Christiano; J. Leike; R. Lowe (2022) arXiv |\nRaw: L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe. Training language models to follow instructions with human feedback, 2022. URL https://arxiv.org/abs/2203.02155\nMatch: Training Language Models to Follow Instructions with Human Feedback\nVenue: Advances in Neural Information Processing Systems 35 DOI: 10.52202/068431-2011 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nYoung people’s use of generative ai to support literacy in 2025I. Picton; C. Clark; F. Bonafede (2025) |\nRaw: I. Picton, C. Clark, and F. Bonafede. Young people’s use of generative ai to support literacy in 2025, 2025. URL https://files.eric.ed.gov/fulltext/ED675268.pdf\nMatch: Young People's Use of Generative AI to Support Literacy in 2025\nVenue: National Literacy Trust / ERIC DOI: ISBN: URL:\nThe cited report exists in ERIC under accession number ED675268 (files.eric.ed.gov/fulltext/ED675268.pdf) published by the National Literacy Trust in 2025.\n|\n|\n|\nVerified\nEdited\n|\nPersonalizing reinforcement learning from human feedback with variational preference learningS. Poddar; Y. Wan; H. Ivison; A. Gupta; N. Jaques (2024) Neural Information Processing Systems (NeurIPS) - Spotlight |\nRaw: S. Poddar, Y. Wan, H. Ivison, A. Gupta, and N. Jaques. Personalizing reinforcement learning from human feedback with variational preference learning. Neural Information Processing Systems (NeurIPS) - Spotlight, 2024.\nMatch: Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning\nVenue: Advances in Neural Information Processing Systems 37 DOI: 10.52202/079017-1664 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nSentence-bert: Sentence embeddings using siamese bert-networksN. Reimers; I. Gurevych (2019) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing |\nRaw: N. Reimers and I. Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, 2019a.\nMatch: Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks\nVenue: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) DOI: 10.18653/v1/d19-1410 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nAuditing radicalization pathways on youtubeM. H. Ribeiro; R. Ottoni; R. West; V. A. Almeida; W. Meira Jr. (2020) Proceedings of the 2020 conference on fairness, accountability, and transparency |\nRaw: M. H. Ribeiro, R. Ottoni, R. West, V. A. Almeida, and W. Meira Jr. Auditing radicalization pathways on youtube. In Proceedings of the 2020 conference on fairness, accountability, and transparency, pages 131-141, 2020.\nMatch: Auditing radicalization pathways on YouTube\nVenue: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20) DOI: 10.1145/3351095.3372879 ISBN: URL:\nThe paper exists exactly as cited in the ACM Digital Library at DOI: 10.1145/3351095.3372879, published in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20), pages 131–141.\n|\n|\n|\nVerified\nEdited\n|\nHuman decision-making is susceptible to ai-driven manipulationS. Sabour; J. M. Liu; S. Liu; C. Z. Yao; S. Cui; X. Zhang; W. Zhang; Y. Cao; A. Bhat; J. Guan; W. Wu; R. Mihalcea; H. Wang; T. Althoff; T. M. C. Lee; M. Huang (2025) arXiv |\nRaw: S. Sabour, J. M. Liu, S. Liu, C. Z. Yao, S. Cui, X. Zhang, W. Zhang, Y. Cao, A. Bhat, J. Guan, W. Wu, R. Mihalcea, H. Wang, T. Althoff, T. M. C. Lee, and M. Huang. Human decision-making is susceptible to ai-driven manipulation, 2025. URL https://arxiv.org/abs/2502.07663.\nMatch: Human Decision-making is Susceptible to AI-driven Manipulation\nVenue: arXiv DOI: 10.48550/arXiv.2502.07663 ISBN: URL:\nVerified via arXiv id 2502.07663.\n|\n|\n|\nVerified\nEdited\n|\nThe impact of generative ai on academic reading and writing: A synthesis of recent evidence (2023-2025)A. Sanz-Tejeda; J. C. Domínguez-Oller; J. M. Baldaquí-Escandell; R. Gómez-Díaz; A. GarcíaRodríguez (2026) Frontiers in Education |\nRaw: A. Sanz-Tejeda, J. C. Domínguez-Oller, J. M. Baldaquí-Escandell, R. Gómez-Díaz, and A. GarcíaRodríguez. The impact of generative ai on academic reading and writing: A synthesis of recent evidence (2023-2025). Frontiers in Education, 10, 2026. doi: 10.3389/feduc.2025.1711718. URL https://www.frontiersin.org/articles/10.3389/feduc.2025.1711718.\nMatch: The impact of generative AI on academic reading and writing: a synthesis of recent evidence (2023–2025)\nVenue: Frontiers in Education DOI: 10.3389/feduc.2025.1711718 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nAi found to boost individual creativity - at the expense of less varied contentScienceDaily (2024) |\nRaw: ScienceDaily. Ai found to boost individual creativity - at the expense of less varied content, 2024. URL https://www.sciencedaily.com/releases/2024/07/240712222127.htm\nMatch: AI found to boost individual creativity -- at the expense of less varied content\nVenue: ScienceDaily DOI: ISBN: URL:\nDirect confirmation from ScienceDaily release page. The URL and title match the published 2024 news article exactly.\n|\n|\n|\nVerified\nEdited\n|\nDistributional preference learning: Understanding and accounting for hidden context in rlhfA. Siththaranjan; C. Laidlaw; D. Hadfield-Menell (2023) arXiv |\nRaw: A. Siththaranjan, C. Laidlaw, and D. Hadfield-Menell. Distributional preference learning: Understanding and accounting for hidden context in rlhf. arXiv preprint arXiv:2312.08358, 2023.\nMatch: Distributional Preference Learning: Understanding and Accounting for Hidden Context in RLHF\nVenue: arXiv DOI: 10.48550/arXiv.2312.08358 ISBN: URL:\nVerified via arXiv id 2312.08358.\n|\n|\n|\nVerified\nEdited\n|\nA roadmap to pluralistic alignmentT. Sorensen; J. Moore; J. Fisher; M. Gordon; N. Mireshghallah; C. M. Rytting; A. Ye; L. Jiang; X. Lu; N. Dziri (2024) arXiv |\nRaw: T. Sorensen, J. Moore, J. Fisher, M. Gordon, N. Mireshghallah, C. M. Rytting, A. Ye, L. Jiang, X. Lu, N. Dziri, et al. A roadmap to pluralistic alignment. arXiv preprint arXiv:2402.05070, 2024.\nMatch: A Roadmap to Pluralistic Alignment\nVenue: arXiv DOI: 10.48550/arXiv.2402.05070 ISBN: URL:\nVerified via arXiv id 2402.05070.\n|\n|\n|\nVerified\nEdited\n|\nThe homogenizing effect of large language models on human expression and thoughtZ. Sourati; A. S. Ziabari; M. Dehghani (2026) arXiv |\nRaw: Z. Sourati, A. S. Ziabari, and M. Dehghani. The homogenizing effect of large language models on human expression and thought, 2026. URL https://arxiv.org/abs/2508.01491.\nMatch: The homogenizing effect of large language models on human expression and thought\nVenue: Trends in Cognitive Sciences DOI: 10.1016/j.tics.2026.01.003 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nThe psychological meaning of words: Liwc and computerized text analysis methodsY. R. Tausczik; J. W. Pennebaker (2010) Journal of Language and Social Psychology |\nRaw: Y. R. Tausczik and J. W. Pennebaker. The psychological meaning of words: Liwc and computerized text analysis methods. Journal of Language and Social Psychology, 29(1):24-54, 2010. doi: 10.1177/0261927X09351676. URL https://doi.org/10.1177/0261927X09351676.\nMatch: The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods\nVenue: Journal of Language and Social Psychology DOI: 10.1177/0261927x09351676 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nApproaching the integration of large language models in the parliamentary workspaceJ. von Lucke Frontiers in Political Science |\nRaw: J. von Lucke. Approaching the integration of large language models in the parliamentary workspace. Frontiers in Political Science, 7:1625394.\nMatch: Approaching the integration of large language models in the parliamentary workspace\nVenue: Frontiers in Political Science DOI: 10.3389/fpos.2025.1625394 ISBN: URL:\nVerified via static CrossRef title search (score: 0.95)\n|\n|\n|\nVerified\nEdited\n|\nThe effect of chatgpt on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysisJ. Wang; W. Fan (2025) Humanities and Social Sciences Communications |\nRaw: J. Wang and W. Fan. The effect of chatgpt on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanities and Social Sciences Communications, 12, 05 2025. doi: 10.1057/s41599-025-04787-y.\nMatch: RETRACTED ARTICLE: The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis\nVenue: Humanities and Social Sciences Communications DOI: 10.1057/s41599-025-04787-y ISBN: URL:\nVerified via static CrossRef title search (score: 0.96)\n|\n|\n|\nVerified\nEdited\n|\nWho wrote this? how smart replies impact language and agency in the workplaceK. Wenker (2023) Telematics and Informatics Reports |\nRaw: K. Wenker. Who wrote this? how smart replies impact language and agency in the workplace. Telematics and Informatics Reports, 10:100062, 2023. doi: 10.1016/j.teler.2023.100062. URL https://doi.org/10.1016/j.teler.2023.100062.\nMatch: Who wrote this? How smart replies impact language and agency in the workplace\nVenue: Telematics and Informatics Reports DOI: 10.1016/j.teler.2023.100062 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nEchoes in ai: Quantifying lack of plot diversity in llm outputsW. Xu; N. Jojic; S. Rao; C. Brockett; B. Dolan (2025) Proceedings of the National Academy of Sciences |\nRaw: W. Xu, N. Jojic, S. Rao, C. Brockett, and B. Dolan. Echoes in ai: Quantifying lack of plot diversity in llm outputs. Proceedings of the National Academy of Sciences, 122(35):e2504966122, 2025. doi: 10.1073/pnas.2504966122. URL https://www.pnas.org/doi/abs/10.1073/pnas.2504966122.\nMatch: Echoes in AI: Quantifying lack of plot diversity in LLM outputs\nVenue: Proceedings of the National Academy of Sciences DOI: 10.1073/pnas.2504966122 ISBN: URL:\nVerified via static CrossRef title search (score: 1.00)\n|\n|\n|\nVerified\nEdited\n|\nEmpirical evidence of large language model’s influence on human spoken communicationH. Yakura; E. Lopez-Lopez; L. Brinkmann; I. Serna; P. Gupta; I. Soraperra; I. Rahwan (2024) arXiv |\nRaw: H. Yakura, E. Lopez-Lopez, L. Brinkmann, I. Serna, P. Gupta, I. Soraperra, and I. Rahwan. Empirical evidence of large language model’s influence on human spoken communication. arXiv preprint arXiv:2409.01754, 2024.\nMatch: Empirical evidence of Large Language Model's influence on human spoken communication\nVenue: arXiv DOI: 10.48550/arXiv.2409.01754 ISBN: URL:\nVerified via arXiv id 2409.01754.\n|", "url": "https://wpnews.pro/news/a-google-deepmind-paper-has-multiple-hallucinated-references", "canonical_source": "https://veruscite-data.com/share/-7KyAgTQ6aqub5ilvQ_6_SVg2wkbjhp95pLeG3QFa-o", "published_at": "2026-08-23 14:37:05+00:00", "updated_at": "2026-08-23 14:44:07.941495+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-research", "ai-ethics"], "entities": ["Google DeepMind", "National Bureau of Economic Research", "arXiv", "Aaron Chatterji", "Erik Brynjolfsson", "Diane Litman", "Fan Zhang", "Pattie Maes"], "alternates": {"html": "https://wpnews.pro/news/a-google-deepmind-paper-has-multiple-hallucinated-references", "markdown": "https://wpnews.pro/news/a-google-deepmind-paper-has-multiple-hallucinated-references.md", "text": "https://wpnews.pro/news/a-google-deepmind-paper-has-multiple-hallucinated-references.txt", "jsonld": "https://wpnews.pro/news/a-google-deepmind-paper-has-multiple-hallucinated-references.jsonld"}}