{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T22:03:28Z","timestamp":1757628208361,"version":"3.44.0"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032028129"},{"type":"electronic","value":"9783032028136"}],"license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"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\/978-3-032-02813-6_25","type":"book-chapter","created":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T07:15:54Z","timestamp":1756624554000},"page":"294-297","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Positional Overload: Positional Debiasing and\u00a0Context Window Extension for\u00a0Large Language Models Using Set Encoding"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8275-1284","authenticated-orcid":false,"given":"Lukas","family":"Kinder","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8215-3614","authenticated-orcid":false,"given":"Lukas","family":"Edman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4891-682X","authenticated-orcid":false,"given":"Alexander","family":"Fraser","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0576-7457","authenticated-orcid":false,"given":"Tobias","family":"K\u00e4fer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"25_CR1","unstructured":"Chen, Y., et al.: Longlora: efficient fine-tuning of long-context large language models. arXiv preprint arXiv:2309.12307 (2023)"},{"key":"25_CR2","unstructured":"Hendrycks, D., et al.: Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300 (2020)"},{"key":"25_CR3","unstructured":"Hsieh, C.P., et al.: Ruler: what\u2019s the real context size of your long-context language models? arXiv preprint arXiv:2404.06654 (2024)"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Pezeshkpour, P., Hruschka, E.: Large language models sensitivity to the order of options in multiple-choice questions. arXiv preprint arXiv:2308.11483 (2023)","DOI":"10.18653\/v1\/2024.findings-naacl.130"},{"key":"25_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127063","volume":"568","author":"J Su","year":"2024","unstructured":"Su, J., Ahmed, M., Lu, Y., Pan, S., Bo, W., Liu, Y.: Roformer: enhanced transformer with rotary position embedding. Neurocomputing 568, 127063 (2024)","journal-title":"Neurocomputing"},{"key":"25_CR6","unstructured":"Touvron, H., et\u00a0al.: Llama: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"key":"25_CR7","unstructured":"Tworkowski, S., Staniszewski, K., Pacek, M., Wu, Y., Michalewski, H., Mi\u0142o\u015b, P.: Focused transformer: contrastive training for context scaling. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"25_CR8","unstructured":"Wang, X., Hu, C., Ma, B., R\u00f6ttger, P., Plank, B.: Look at the text: instruction-tuned language models are more robust multiple choice selectors than you think. arXiv preprint arXiv:2404.08382 (2024)"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Wei, S.L., Wu, C.K., Huang, H.H., Chen, H.H.: Unveiling selection biases: exploring order and token sensitivity in large language models. arXiv preprint arXiv:2406.03009 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.333"},{"key":"25_CR10","unstructured":"Zhao, Z., Wallace, E., Feng, S., Klein, D., Singh, S.: Calibrate before use: improving few-shot performance of language models. In: International Conference on Machine Learning, pp. 12697\u201312706. PMLR (2021)"},{"key":"25_CR11","unstructured":"Zheng, C., Zhou, H., Meng, F., Zhou, J., Huang, M.: Large language models are not robust multiple choice selectors. In: The Twelfth International Conference on Learning Representations (2023)"},{"key":"25_CR12","unstructured":"Zhu, D., et al.: Pose: Efficient context window extension of LLMs via positional skip-wise training. arXiv preprint arXiv:2309.10400 (2023)"}],"container-title":["Lecture Notes in Computer Science","KI 2025: Advances in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-02813-6_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T06:03:14Z","timestamp":1757484194000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-02813-6_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"ISBN":["9783032028129","9783032028136"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-02813-6_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,1]]},"assertion":[{"value":"1 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"KI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"German Conference on Artificial Intelligence (K\u00fcnstliche Intelligenz)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Potsdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"48","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ki2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ki2025.gi.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}