{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T03:52:10Z","timestamp":1767066730562,"version":"3.48.0"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:00:00Z","timestamp":1767052800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:00:00Z","timestamp":1767052800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["No. MOST 107-2410-H-006 040-MY3 and MOST 108-2511-H-0 06-009"],"award-info":[{"award-number":["No. MOST 107-2410-H-006 040-MY3 and MOST 108-2511-H-0 06-009"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1007\/s10115-025-02612-1","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T03:03:42Z","timestamp":1767063822000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Avoiding toxicity and prejudice in chatbots with knowledge distillation"],"prefix":"10.1007","volume":"68","author":[{"given":"Hei-Chia","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cendra Devayana","family":"Putra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hsin-Tzu","family":"Weng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,30]]},"reference":[{"key":"2612_CR1","doi-asserted-by":"crossref","unstructured":"Dinan E et al. (2019) Build it break it fix it for dialogue safety: Robustness from adversarial human attack. arXiv preprint https:\/\/arxiv.org\/abs\/1908.06083","DOI":"10.18653\/v1\/D19-1461"},{"issue":"1","key":"2612_CR2","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s00146-020-01053-4","volume":"36","author":"T Zem\u010d\u00edk","year":"2021","unstructured":"Zem\u010d\u00edk T (2021) Failure of chatbot Tay was evil, ugliness and uselessness in its nature or do we judge it through cognitive shortcuts and biases? AI Soc 36(1):361\u2013367","journal-title":"AI Soc"},{"key":"2612_CR3","unstructured":"Bubniuk N (2017) What is Microsoft AI Tay bot?"},{"key":"2612_CR4","unstructured":"Lomas N (2017) Another AI chatbot shown spouting offensive views. https:\/\/techcrunch.com\/2017\/10\/24\/another-ai-chatbot-shown-spouting-offensive-views\/?guccounter=1"},{"key":"2612_CR5","unstructured":"Allen K (2017) Chinese chatbots shut down after anti-government posts. https:\/\/www.bbc.com\/news\/world-asia-china-40815024"},{"key":"2612_CR6","unstructured":"Kim D (2021) Chatbot gone awry starts conversations about AI ethics in South Korea. https:\/\/thediplomat.com\/2021\/01\/chatbot-gone-awry-starts-conversations-about-ai-ethics-in-south-korea\/"},{"key":"2612_CR7","unstructured":"Xu J et al. (2020) Recipes for safety in open-domain chatbots. arXiv preprint https:\/\/arxiv.org\/abs\/2010.07079"},{"key":"2612_CR8","doi-asserted-by":"crossref","unstructured":"Brassard-Gourdeau E, Khoury R (2019) Subversive toxicity detection using sentiment information. In: proceedings of the third workshop on abusive language online","DOI":"10.18653\/v1\/W19-3501"},{"key":"2612_CR9","doi-asserted-by":"crossref","unstructured":"Hu M et al. (2018) Attention-guided answer distillation for machine reading comprehension. arXiv preprint https:\/\/arxiv.org\/abs\/1808.07644","DOI":"10.18653\/v1\/D18-1232"},{"issue":"5","key":"2612_CR10","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1007\/s10115-022-01667-8","volume":"64","author":"JW Lee","year":"2022","unstructured":"Lee JW et al (2022) Knowledge distillation meets recommendation: collaborative distillation for top-N recommendation. Knowl Inf Syst 64(5):1323\u20131348","journal-title":"Knowl Inf Syst"},{"issue":"7","key":"2612_CR11","doi-asserted-by":"publisher","first-page":"779","DOI":"10.3390\/electronics10070779","volume":"10","author":"D Dess\u00ec","year":"2021","unstructured":"Dess\u00ec D, Recupero DR, Sack H (2021) An assessment of deep learning models and word embeddings for toxicity detection within online textual comments. Electronics 10(7):779","journal-title":"Electronics"},{"key":"2612_CR12","unstructured":"Pavlopoulos J et al. Toxicity detection: Does context really matter? arXiv preprint https:\/\/arxiv.org\/abs\/2006.00998"},{"key":"2612_CR13","doi-asserted-by":"crossref","unstructured":"Yousafzai F et al. (2023) Applications of AG-groupoids in decision-making via linear diophantine fuzzy sets. Discrete Dynamics in Nature and Society, 2023","DOI":"10.1155\/2023\/3411475"},{"issue":"4","key":"2612_CR14","doi-asserted-by":"publisher","first-page":"819","DOI":"10.1007\/s10115-020-01534-4","volume":"63","author":"X Hu","year":"2021","unstructured":"Hu X, Duan JL, Dang DP (2021) Natural language question answering over knowledge graph: the marriage of SPARQL query and keyword search. Knowl Inf Syst 63(4):819\u2013844","journal-title":"Knowl Inf Syst"},{"key":"2612_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-023-01966-8","author":"A Formica","year":"2023","unstructured":"Formica A, Mele I, Taglino F (2023) A template-based approach for question answering over knowledge bases. Knowl Inf Syst. https:\/\/doi.org\/10.1007\/s10115-023-01966-8","journal-title":"Knowl Inf Syst"},{"key":"2612_CR16","doi-asserted-by":"publisher","DOI":"10.1145\/3527450","author":"Q Motger","year":"2023","unstructured":"Motger Q, Franch X, Marco J (2023) Software-based dialogue systems: survey, taxonomy, and challenges. ACM Comput Surv. https:\/\/doi.org\/10.1145\/3527450","journal-title":"ACM Comput Surv"},{"key":"2612_CR17","doi-asserted-by":"publisher","DOI":"10.1111\/ijcs.12933","author":"K Gopinath","year":"2023","unstructured":"Gopinath K, Kasilingam D (2023) Antecedents of intention to use chatbots in service encounters: a meta-analytic review. Int J Consum Stud. https:\/\/doi.org\/10.1111\/ijcs.12933","journal-title":"Int J Consum Stud"},{"key":"2612_CR18","unstructured":"Chung J et al. (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint https:\/\/arxiv.org\/abs\/1412.3555"},{"key":"2612_CR19","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst"},{"issue":"5","key":"2612_CR20","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1007\/s10439-023-03196-z","volume":"51","author":"K Cheng","year":"2023","unstructured":"Cheng K et al (2023) Talk with ChatGPT about the outbreak of Mpox in 2022: reflections and suggestions from AI dimensions. Ann Biomed Eng 51(5):870\u2013874","journal-title":"Ann Biomed Eng"},{"key":"2612_CR21","doi-asserted-by":"crossref","unstructured":"Han KSS, Myint MT (2024) A comparative study of LSTM, Bi-LSTM, and BERT for automated essay scoring. In: 2024 5th international conference on advanced information technologies (ICAIT)","DOI":"10.1109\/ICAIT65209.2024.10754925"},{"key":"2612_CR22","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1016\/j.ins.2022.03.038","volume":"596","author":"L Zhou","year":"2022","unstructured":"Zhou L et al (2022) Attention-based BiLSTM models for personality recognition from user-generated content. Inf Sci 596:460\u2013471","journal-title":"Inf Sci"},{"key":"2612_CR23","unstructured":"Ezen-Can A (2020) A Comparison of LSTM and BERT for Small Corpus. arXiv preprint https:\/\/arxiv.org\/abs\/2009.05451"},{"key":"2612_CR24","doi-asserted-by":"crossref","unstructured":"Cho K et al. (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint https:\/\/arxiv.org\/abs\/1406.1078","DOI":"10.3115\/v1\/D14-1179"},{"key":"2612_CR25","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv preprint https:\/\/arxiv.org\/abs\/1503.02531"},{"issue":"11","key":"2612_CR26","doi-asserted-by":"publisher","first-page":"3113","DOI":"10.1007\/s10115-022-01736-y","volume":"64","author":"M Ryu","year":"2022","unstructured":"Ryu M, Lee G, Lee K (2022) Knowledge distillation for BERT unsupervised domain adaptation. Knowl Inf Syst 64(11):3113\u20133128","journal-title":"Knowl Inf Syst"},{"key":"2612_CR27","unstructured":"Ma H et al. (2021) Undistillable: making a nasty teacher that CANNOT teach students. arXiv preprint https:\/\/arxiv.org\/abs\/2105.07381"},{"key":"2612_CR28","doi-asserted-by":"crossref","unstructured":"Kim B et al. (2021) Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation. arXiv preprint https:\/\/arxiv.org\/abs\/2108.12582","DOI":"10.18653\/v1\/2021.findings-emnlp.286"},{"key":"2612_CR29","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1016\/j.neucom.2021.08.131","volume":"465","author":"H Wang","year":"2021","unstructured":"Wang H et al (2021) Towards information-rich, logical dialogue systems with knowledge-enhanced neural models. Neurocomputing 465:248\u2013264","journal-title":"Neurocomputing"},{"key":"2612_CR30","doi-asserted-by":"crossref","unstructured":"Chae H et al. (2023) Dialogue chain-of-thought distillation for commonsense-aware conversational agents. arXiv preprint https:\/\/arxiv.org\/abs\/2310.09343","DOI":"10.18653\/v1\/2023.emnlp-main.342"},{"key":"2612_CR31","doi-asserted-by":"crossref","unstructured":"Qiu H et al. (2024) Facilitating pornographic text detection for open-domain dialogue systems via knowledge distillation of large language models. arXiv preprint https:\/\/arxiv.org\/abs\/2403.13250","DOI":"10.1109\/CSCWD61410.2024.10579996"},{"key":"2612_CR32","unstructured":"Danescu-Niculescu-Mizil C, Lee L (2011) Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs. arXiv preprint https:\/\/arxiv.org\/abs\/1106.3077"},{"key":"2612_CR33","doi-asserted-by":"crossref","unstructured":"Baheti A et al. (2018) Generating more interesting responses in neural conversation models with distributional constraints. arXiv preprint https:\/\/arxiv.org\/abs\/1809.01215","DOI":"10.18653\/v1\/D18-1431"},{"key":"2612_CR34","doi-asserted-by":"crossref","unstructured":"Davidson T et al. (2017) Automated hate speech detection and the problem of offensive language. In: proceedings of the international AAAI conference on web and social media","DOI":"10.1609\/icwsm.v11i1.14955"},{"key":"2612_CR35","doi-asserted-by":"crossref","unstructured":"Lin CY, Och FJ (2004) Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics. Barcelona, Spain. Pp. 605\u2013612","DOI":"10.3115\/1218955.1219032"},{"key":"2612_CR36","doi-asserted-by":"crossref","unstructured":"Sedoc J, Ungar L (2020) Item response theory for efficient human evaluation of chatbots. In: proceedings of the first workshop on evaluation and comparison of NLP systems","DOI":"10.18653\/v1\/2020.eval4nlp-1.3"},{"key":"2612_CR37","unstructured":"Liang H, Li H (2021) Towards Standard Criteria for human evaluation of Chatbots: A Survey. arXiv preprint arXiv:2105.11197, 2021."},{"key":"2612_CR38","doi-asserted-by":"crossref","unstructured":"Papernot N et al. (2016) Distillation as a defense to adversarial perturbations against deep neural networks. In: 2016 IEEE symposium on security and privacy (SP). IEEE.","DOI":"10.1109\/SP.2016.41"},{"key":"2612_CR39","doi-asserted-by":"crossref","unstructured":"Wang X et al. (2023) How to distill your BERT: an empirical study on the impact of weight initialisation and distillation objectives","DOI":"10.18653\/v1\/2023.acl-short.157"},{"key":"2612_CR40","doi-asserted-by":"publisher","DOI":"10.3390\/socsci11010023","author":"R Bridgelall","year":"2022","unstructured":"Bridgelall R (2022) An application of natural language processing to classify what terrorists say they want. Soc Sci. https:\/\/doi.org\/10.3390\/socsci11010023","journal-title":"Soc Sci"},{"key":"2612_CR41","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/j.procs.2015.03.085","volume":"45","author":"BS Nandhini","year":"2015","unstructured":"Nandhini BS, Sheeba JI (2015) Online social network bullying detection using intelligence techniques. Procedia Comput Sci 45:485\u2013492","journal-title":"Procedia Comput Sci"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02612-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-025-02612-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-025-02612-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T03:03:56Z","timestamp":1767063836000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-025-02612-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,30]]},"references-count":41,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["2612"],"URL":"https:\/\/doi.org\/10.1007\/s10115-025-02612-1","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"type":"print","value":"0219-1377"},{"type":"electronic","value":"0219-3116"}],"subject":[],"published":{"date-parts":[[2025,12,30]]},"assertion":[{"value":"1 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests\/personal relationships which may be considered as potential competing interests:","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}],"article-number":"30"}}