{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T04:17:50Z","timestamp":1749615470857,"version":"3.41.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031880353"},{"type":"electronic","value":"9783031880360"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-88036-0_6","type":"book-chapter","created":{"date-parts":[[2025,4,14]],"date-time":"2025-04-14T08:07:40Z","timestamp":1744618060000},"page":"120-130","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Shrink the\u00a0Longest: Improving Latent Space Isotropy with\u00a0Simplicial Geometry"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1899-4405","authenticated-orcid":false,"given":"Sergej","family":"Kudrjashov","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0477-1502","authenticated-orcid":false,"given":"Olesya","family":"Karpik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4020-488X","authenticated-orcid":false,"given":"Eduard","family":"Klyshinsky","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,15]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/s10844-017-0473-4","volume":"52","author":"N Atienza","year":"2019","unstructured":"Atienza, N., Gonzalez-Diaz, R., Rucco, M.: Persistent entropy for separating topological features from noise in Vietoris-rips complexes. J. Intell. Inf. Syst. 52, 637\u2013655 (2019)","journal-title":"J. Intell. Inf. Syst."},{"key":"6_CR2","unstructured":"Cai, X., Huang, J., Bian, Y.-L., Church, K.W.: Isotropy in the contextual embedding space: clusters and manifolds. In: International Conference on Learning Representations (2021)"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Demeter, D., Kimmel, G.J., Downey, D.: Stolen probability: a structural weakness of neural language models. In: Annual Meeting of the Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.198"},{"key":"6_CR4","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: North American Chapter of the Association for Computational Linguistics (2019)"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Ding, Y., Martinkus, K., Pascual, D., Clematide, S., Wattenhofer, R.: On isotropy calibration of transformer models. In: Proceedings of the Third Workshop on Insights from Negative Results in NLP, pp. 1\u20139. Association for Computational Linguistics (2022)","DOI":"10.18653\/v1\/2022.insights-1.1"},{"key":"6_CR6","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 the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 55\u201365. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/D19-1006"},{"key":"6_CR7","unstructured":"Gao, J., He, D., Tan, X., Qin, T., Wang, L., Liu, T.-Y.: Representation degeneration problem in training natural language generation models. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019 (2019)"},{"key":"6_CR8","unstructured":"Godey, N., Villemonte de la Clergerie, E., Sagot, B.: Anisotropy is inherent to self-attention in transformers. In: Conference of the European Chapter of the Association for Computational Linguistics (2024)"},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Huang, J., et al.: WhiteningBERT: an easy unsupervised sentence embedding approach. In: Conference on Empirical Methods in Natural Language Processing (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.23"},{"key":"6_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"6_CR11","doi-asserted-by":"publisher","first-page":"1069","DOI":"10.1007\/s10208-021-09522-y","volume":"22","author":"J Leygonie","year":"2021","unstructured":"Leygonie, J., Oudot, S., Tillmann, U.: A framework for differential calculus on persistence barcodes. Found. Comput. Math. 22, 1069\u20131131 (2021)","journal-title":"Found. Comput. Math."},{"key":"6_CR12","doi-asserted-by":"crossref","unstructured":"Li, B., Zhou, H., He, J., Wang, M., Yang, Y., Li, L.: On the sentence embeddings from pre-trained language models. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9119\u20139130. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.733"},{"key":"6_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1007\/978-3-030-86383-8_36","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2021","author":"Y Liang","year":"2021","unstructured":"Liang, Y., Cao, R., Zheng, J., Ren, J., Gao, L.: Learning to remove: towards isotropic pre-trained BERT embedding. In: Farka\u0161, I., Masulli, P., Otte, S., Wermter, S. (eds.) ICANN 2021. LNCS, vol. 12895, pp. 448\u2013459. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-86383-8_36"},{"key":"6_CR14","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"6_CR15","unstructured":"Moor, M., Horn, M., Rieck, B., Borgwardt, K.: Topological autoencoders. In: Proceedings of the 37th International Conference on Machine Learning, pp. 7045\u20137054. PMLR (2020)"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Press, O., Wolf, L.: Using the output embedding to improve language models. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, Valencia, Spain, ACL, pp. 157\u2013163 (2017)","DOI":"10.18653\/v1\/E17-2025"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Rajaee, S., Pilehvar, M.: How does fine-tuning affect the geometry of embedding space: a case study on isotropy. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 3042\u20133049. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.261"},{"key":"6_CR18","unstructured":"Razzhigaev, A., Mikhalchuk, M., Goncharova, E., Oseledets, I., Dimitrov, D., Kuznetsov, A.: The shape of learning: anisotropy and intrinsic dimensions in transformer-based models. In: Findings of the Association for Computational Linguistics: EACL 2024, pp. 868\u2013874 (2024)"},{"key":"6_CR19","unstructured":"Trofimov, I., Cherniavskii, D., Tulchinskii, E., Balabin, N., Burnaev, E., Barannikov, S.: Learning topology-preserving data representations. In: The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, 1\u20135 May 2023 (2023)"},{"key":"6_CR20","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NEURIPS (2017)"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.: GLUE: a multi-task benchmark and analysis platform for natural language understanding. In: Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pp. 353\u2013355. Association for Computational Linguistics (2018)","DOI":"10.18653\/v1\/W18-5446"},{"key":"6_CR22","unstructured":"Wang, L., Huang, J., Huang, K., Hu, Z., Wang, G., Gu, Q.: Improving neural language generation with spectrum control. In: Proceedings of International Conference on Learning Representations (2020)"},{"key":"6_CR23","unstructured":"Xu, J., Sun, X., Zhang, Z., Zhao, G., Lin, J.: Understanding and improving layer normalization. arXiv preprint arXiv:1911.07013 (2019)"},{"key":"6_CR24","unstructured":"Yinhan, L., et al.: RoBERTa: A Robustly Optimized BERT Pretraining Approach (2019)"},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, L., Buntine, W., Shareghi, E.: On the effect of isotropy on VAE representations of text. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 694\u2013701. Association for Computational Linguistics (2022)","DOI":"10.18653\/v1\/2022.acl-short.78"}],"container-title":["Lecture Notes in Computer Science","Analysis of Images, Social Networks and Texts"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-88036-0_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T02:57:09Z","timestamp":1749610629000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-88036-0_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031880353","9783031880360"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-88036-0_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"15 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIST","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Analysis of Images, Social Networks and Texts","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bishkek","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyrgyzstan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aist2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aistconf.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}