{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T10:29:31Z","timestamp":1777631371219,"version":"3.51.4"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032243492","type":"print"},{"value":"9783032243508","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-24350-8_16","type":"book-chapter","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T03:28:10Z","timestamp":1777433290000},"page":"236-251","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Crystallizing Semantics: Mapping the Journey of Word Meaning in Language Models"],"prefix":"10.1007","author":[{"given":"Himanshu","family":"Dwivedi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"16_CR1","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013). Accessed 20 Aug 2025"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: GloVe: global vectors for word representation. In: Proceedings of EMNLP, pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"issue":"1","key":"16_CR3","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1145\/503104.503110","volume":"20","author":"L Finkelstein","year":"2002","unstructured":"Finkelstein, L., et al.: Placing search in context: the concept revisited. ACM Trans. Inf. Syst. 20(1), 116\u2013131 (2002)","journal-title":"ACM Trans. Inf. Syst."},{"issue":"4","key":"16_CR4","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1162\/COLI_a_00237","volume":"41","author":"F Hill","year":"2015","unstructured":"Hill, F., Reichart, R., Korhonen, A.: SimLex-999: evaluating semantic models with genuine similarity estimation. Comput. Linguist. 41(4), 665\u2013695 (2015)","journal-title":"Comput. Linguist."},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Halawi, G., Dror, G., Gabrilovich, E., Koren, Y.: Large-scale learning of word relatedness with constraints. In: Proceedings of KDD (2012)","DOI":"10.1145\/2339530.2339751"},{"key":"16_CR6","unstructured":"Liu, S., Ye, Y.: Tracing the development of word meaning during training. Stanford CS224N custom project report, Stanford University (2025). https:\/\/web.stanford.edu\/class\/cs224n\/finalreports\/256989451.pdf. Accessed 20 Aug 2025"},{"key":"16_CR7","unstructured":"Biderman, S., Schoelkopf, H., Anthony, Q., Gao, L., et al.: Pythia: a suite for analyzing large language models across training and scaling. arXiv:2304.01373 (2023)"},{"issue":"1","key":"16_CR8","doi-asserted-by":"publisher","first-page":"72","DOI":"10.2307\/1412159","volume":"15","author":"C Spearman","year":"1904","unstructured":"Spearman, C.: The proof and measurement of association between two things. Am. J. Psychol. 15(1), 72\u2013101 (1904)","journal-title":"Am. J. Psychol."},{"issue":"1\u20132","key":"16_CR9","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1093\/biomet\/30.1-2.81","volume":"30","author":"MG Kendall","year":"1938","unstructured":"Kendall, M.G.: A new measure of rank correlation. Biometrika 30(1\u20132), 81\u201393 (1938)","journal-title":"Biometrika"},{"key":"16_CR10","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1162\/tacl_a_00051","volume":"5","author":"P Bojanowski","year":"2017","unstructured":"Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguis. 5, 135\u2013146 (2017)","journal-title":"Trans. Assoc. Comput. Linguis."},{"key":"16_CR11","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30, pp. 5998\u20136008 (2017)"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT 2019, pp. 4171\u20134186 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"16_CR13","unstructured":"Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners. OpenAI Technical Report (2019). https:\/\/cdn.openai.com\/better-languagemodels\/language_models_are_unsupervised_multitask_learners.pdf. Accessed 20 Aug 2025"},{"key":"16_CR14","unstructured":"Brown, T.B., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems (NeurIPS 2020), vol. 33, pp. 1877\u20131901 (2020)"},{"key":"16_CR15","unstructured":"Reif, E., et al.: Visualizing and measuring the geometry of BERT. In: Advances in Neural Information Processing Systems (NeurIPS 2019), vol. 32, pp. 8594\u20138603 (2019)"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Ethayarajh, K.: How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings. In: Proceedings of EMNLP 2019, pp. 55\u201365 (2019)","DOI":"10.18653\/v1\/D19-1006"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: GLUE: a multi-task benchmark and analysis platform for natural language understanding. In: Proceedings of EMNLP 2018, pp. 353\u2013355 (2018)","DOI":"10.18653\/v1\/W18-5446"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Saphra, N., Lopez, A.: Understanding learning dynamics of language models with SVCCA. In: Proceedings of NAACL-HLT 2019, pp. 3257\u20133267 (2019)","DOI":"10.18653\/v1\/N19-1329"},{"key":"16_CR19","unstructured":"Gao, L., et al.: The Pile: An 800GB dataset of diverse text for language modeling. arXiv:2101.00027 (2021)"},{"key":"16_CR20","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems (NeurIPS 2019), vol. 32, pp. 8024\u20138035 (2019). https:\/\/arxiv.org\/abs\/2101.00027. Accessed 20 Aug 2025"},{"key":"16_CR21","unstructured":"Wolf, T., et al.: Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 38\u201345 (2020)"},{"key":"16_CR22","unstructured":"Kaplan, J., McCandlish, S., Henighan, T., Brown, T.B., et al.: Scaling laws for neural language models. arXiv:2001.08361 (2020). Accessed 20 Aug 2025"},{"key":"16_CR23","unstructured":"Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., et al.: Training compute-optimal large language models. arXiv:2203.15556 (2022). Accessed 20 Aug 2025"},{"key":"16_CR24","unstructured":"Kornblith, S., Norouzi, M., Lee, H., Hinton, G.: Similarity of neural network representations revisited. In: Proceedings of ICML (2019)"},{"key":"16_CR25","unstructured":"Raghu, M., Gilmer, J., Yosinski, J., Sohl-Dickstein, M.: SVCCA: singular vector canonical correlation analysis for deep learning dynamics and interpretability. In: Advances in Neural Information Processing Systems (2017)"},{"key":"16_CR26","unstructured":"Morcos, A.S., Raghu, M., Bengio, S.: Insights on representational similarity in neural networks with canonical correlation. In: Advances in Neural Information Processing Systems (2018)"},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Radinsky, K., Agichtein, E., Gabrilovich, E., Markovitch, S.: A word at a time: computing word relatedness using temporal semantic analysis. In: Proceedings of the 20th International Conference on World Wide Web (WWW 2011), pp. 337\u2013346 (2011)","DOI":"10.1145\/1963405.1963455"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Music, Sound, Art and Design"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-24350-8_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T03:28:41Z","timestamp":1777433321000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-24350-8_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032243492","9783032243508"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-24350-8_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"30 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This work is intended as a research contribution and does not involve human subjects, personal data, or sensitive information beyond the use of publicly available datasets (WordSim-353, SimLex-999, MTurk-771). All datasets are widely used in the NLP community under academic-use terms, and we adhered to their licensing conditions.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Statement"}},{"value":"The models analyzed in this study (Pythia-70M and Pythia-410M) are openly released checkpoints from EleutherAI, trained on The Pile, a publicly available corpus. We did not perform additional training or data collection.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Statement"}},{"value":"We emphasize that our findings are intended for scientific analysis of representation dynamics, not for deployment or downstream applications. No claims are made regarding the suitability of these models for end-user systems, and care should be taken to avoid over-interpreting correlation with human judgments as an indication of safety or reliability in applied settings.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Statement"}},{"value":"EvoMUSART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toulouse","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 April 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"evomusart2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.evostar.org\/2026\/evomusart\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}