{"id":5031,"date":"2026-02-12T11:59:05","date_gmt":"2026-02-12T10:59:05","guid":{"rendered":"https:\/\/hci.hs-kl.de\/?p=5031"},"modified":"2026-03-11T14:32:12","modified_gmt":"2026-03-11T13:32:12","slug":"slm","status":"publish","type":"post","link":"https:\/\/hci.hs-kl.de\/index.php\/2026\/02\/12\/slm\/","title":{"rendered":"Small Language Models im medizinischen Kontext"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"5031\" class=\"elementor elementor-5031\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2779b311 e-flex e-con-boxed e-con e-parent\" data-id=\"2779b311\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-956c7ab elementor-widget elementor-widget-heading\" data-id=\"956c7ab\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Small Language Models im medizinischen Kontext<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7b083d66 elementor-widget elementor-widget-text-editor\" data-id=\"7b083d66\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p data-start=\"2801\" data-end=\"3077\">Im HCI2B-Labor pr\u00e4sentierte Nicolas Ventulett seinen Vortrag \u201eBeyond Single Models: Unsupervised Ensemble Selection for Small Language Models in Medical QA\u201c, den er zuvor beim URAI Symposium (Upper-Rhine Artificial Intelligence Symposium) in Stra\u00dfburg vorgestellt hatte.<\/p><p data-start=\"3079\" data-end=\"3486\">Im Mittelpunkt seines Beitrags stand die Frage, wie medizinische Frage-Antwort-Systeme auf Basis klinischer Berichte robuster, zuverl\u00e4ssiger und gleichzeitig ressourcenschonender gestaltet werden k\u00f6nnen. W\u00e4hrend aktuelle Entwicklungen h\u00e4ufig auf sehr gro\u00dfe Sprachmodelle setzen, untersucht Nicolas einen alternativen Ansatz: den Einsatz mehrerer kompakter Small Language Models (SLMs) in einem Ensemble.<\/p><p data-start=\"3488\" data-end=\"3862\">Die Grundidee besteht darin, mehrere Modelle parallel Antworten generieren zu lassen und diese systematisch miteinander zu vergleichen. Mithilfe trainingsfreier Auswahlmechanismen wird anschlie\u00dfend die jeweils \u00fcberzeugendste Antwort bestimmt. Dieser Ansatz kann nicht nur die Qualit\u00e4t und Stabilit\u00e4t der Ergebnisse erh\u00f6hen, sondern auch Transparenz und Kontrolle verbessern.<\/p><p data-start=\"3864\" data-end=\"4150\">Gerade im medizinischen Kontext \u2013 einem stark regulierten und sensiblen Anwendungsbereich \u2013 sind Datenschutz, Nachvollziehbarkeit und Ressourceneffizienz zentrale Anforderungen. Die vorgestellte Methodik bietet hier vielversprechende Perspektiven f\u00fcr lokale, kontrollierbare KI-Systeme.<\/p><p data-start=\"4152\" data-end=\"4331\">Der Vortrag regte eine intensive Diskussion im Labor an und lieferte wertvolle Impulse f\u00fcr zuk\u00fcnftige Forschungsarbeiten im Bereich interaktiver und vertrauensw\u00fcrdiger KI-Systeme.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d33d51a elementor-widget elementor-widget-spacer\" data-id=\"d33d51a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bcbed4b elementor-arrows-position-inside elementor-pagination-position-outside elementor-widget elementor-widget-image-carousel\" data-id=\"bcbed4b\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;slides_to_show&quot;:&quot;1&quot;,&quot;navigation&quot;:&quot;both&quot;,&quot;autoplay&quot;:&quot;yes&quot;,&quot;pause_on_hover&quot;:&quot;yes&quot;,&quot;pause_on_interaction&quot;:&quot;yes&quot;,&quot;autoplay_speed&quot;:5000,&quot;infinite&quot;:&quot;yes&quot;,&quot;effect&quot;:&quot;slide&quot;,&quot;speed&quot;:500}\" data-widget_type=\"image-carousel.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-image-carousel-wrapper swiper\" role=\"region\" aria-roledescription=\"carousel\" aria-label=\"SLM im medizinischen Kontext\" dir=\"ltr\">\n\t\t\t<div class=\"elementor-image-carousel swiper-wrapper\" aria-live=\"off\">\n\t\t\t\t\t\t\t\t<div class=\"swiper-slide\" role=\"group\" aria-roledescription=\"slide\" aria-label=\"1 von 1\"><figure class=\"swiper-slide-inner\"><img decoding=\"async\" class=\"swiper-slide-image\" src=\"https:\/\/hci.hs-kl.de\/wp-content\/uploads\/2026\/02\/IMG_3530-scaled-e1773235921639.jpg\" alt=\"Nicolas Ventulett stellt sein Small Language Models im medizinischen Kontext vor\" \/><\/figure><\/div>\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Small Language Models im medizinischen Kontext Im HCI2B-Labor pr\u00e4sentierte Nicolas Ventulett seinen Vortrag \u201eBeyond Single Models: Unsupervised Ensemble Selection for Small Language Models in Medical QA\u201c, den er zuvor beim URAI Symposium (Upper-Rhine Artificial Intelligence Symposium) in Stra\u00dfburg vorgestellt hatte. Im Mittelpunkt seines Beitrags stand die Frage, wie medizinische Frage-Antwort-Systeme auf Basis klinischer Berichte robuster,&#8230;<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"iawp_total_views":7,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-5031","post","type-post","status-publish","format-standard","hentry","category-allgemein"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Small Language Models im medizinischen Kontext - HCI2B-Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/hci.hs-kl.de\/index.php\/2026\/02\/12\/slm\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Small Language Models im medizinischen Kontext - HCI2B-Group\" \/>\n<meta property=\"og:description\" content=\"Small Language Models im medizinischen Kontext Im HCI2B-Labor pr\u00e4sentierte Nicolas Ventulett seinen Vortrag \u201eBeyond Single Models: Unsupervised Ensemble Selection for Small Language Models in Medical QA\u201c, den er zuvor beim URAI Symposium (Upper-Rhine Artificial Intelligence Symposium) in Stra\u00dfburg vorgestellt hatte. 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