AIM AI-drevet Multiformatunivers

• Grundetjern, Morten; Andersen, Per-Arne & Goodwin, Morten (2025). Synthetic Personas: Enhancing Demographic Response Simulation through Large Language Models and Genetic Algorithms. International Journal on Cybernetics & Informatics. ISSN 2320-8430. 14(2), s. 21–41. doi: 10.5121/ijci.2025.140202. Fulltekst i vitenarkiv Vis sammendrag

  Understanding diverse demographic groups presents a significant challenge in market research. In this paper, we introduce a novel system that integrates large language models with genetic algorithms to create synthetic personas capable of generating feedback that approximates real-world human responses. Our experimental evaluation demonstrates that synthetic personas not only exhibit age-differentiated technology usage patterns consistent with documented trends but also benefit from genetic algorithm optimization, which improves response accuracy from 60.4% to 78.5% on training questions and from 62.6% to 68.8% on hidden questions—outperforming human estimators. Moreover, the optimized personas achieve a 51.1% better correspondence with actual income distributions compared to random profiles. This approach makes it possible to rapidly generate feedback without requiring participants, facilitates iterative follow-ups, and systematically enhances demographic representativeness.

• Grundetjern, Morten; Andersen, Per-Arne; Goodwin, Morten & Borgersen, Karl Audun (2024). NER Explainability Framework: Utilizing LIME to Enhance Clarity and Robustness in Named Entity Recognition. I Bramer, Max & Stahl, Frederic (Red.), Artificial Intelligence XLI: 44th SGAI International Conference on Artificial Intelligence, AI 2024. Springer. ISSN 9783031779145. s. 3–15. doi: 10.1007/978-3-031-77915-2_1. Fulltekst i vitenarkiv Vis sammendrag

  Abstract. Named Entity Recognition (NER) is crucial for many Nat- ural Language Processing (NLP) applications, yet current models of- ten lack explainability and robustness, particularly against lexical varia- tions. While methods like Local Interpretable Model-agnostic Explana- tions (LIME) have enhanced explainability in text classification, their application to sequence-based tasks like NER remains an open chal- lenge. This paper introduces the NER Explainability Framework (NEF), a novel approach that transforms the sequence-based NER task into a multi-classification problem. This transformation enables the application of LIME to any NER architecture. NEF provides precise, interpretable insights into model decisions by highlighting which parts of the input text most influence tagging decisions, significantly advancing explainability in NER. To address the critical gap in assessing model resilience, we also present the Misspelling Robustness Score (MRS), a new metric designed to quantify NER model resilience against lexical variations, particularly misspellings. MRS offers a comprehensive evaluation of model perfor- mance in the face of spelling variations and errors. We apply NEF to four widely used NER models (SpaCy, Flair, Stanza, and RoBERTa) using the CoNLL-2003 dataset, revealing critical patterns and weaknesses in model behavior. Our method enables targeted improvements, demonstrated by fine-tuning the SpaCy model to achieve a significant 17.5% increase in robustness against misspellings. These findings lay the groundwork for developing more robust, accurate, and interpretable NER systems, pro- viding practical strategies to address existing challenges in NLP. The universal applicability of NEF across NER architectures and the insights provided by MRS offers valuable tools for researchers and practitioners in advancing the field of NER.

Se alle arbeider i NVA

• Grundetjern, Morten (2025). Drømmedagen med Dyreparken (Presentasjon). Fulltekst i vitenarkiv
• Grundetjern, Morten (2025). Nasjonalt nettverkstreff for alumnivirksomhet Presentation. Fulltekst i vitenarkiv
• Grundetjern, Morten (2025). Forsker Grand Prix 2025 Presentation. Fulltekst i vitenarkiv
• Grundetjern, Morten (2024). INRESCOS Match Making Event 2024 Presentation. Fulltekst i vitenarkiv
• Grundetjern, Morten (2024). NORA Annual Conference 2024 Presentation of NER Paper. Fulltekst i vitenarkiv
• Grundetjern, Morten (2024). SGAI 2024 - NER Explainability Framework Presentation at SGAI. Fulltekst i vitenarkiv
• Goodwin, Morten; Nærland, Mina Hauge & Bjørkeng, Per Kristian (2024). Ungt tech-geni frykter at KI tar over innen fem år. [Journal]. Aftenposten. Fulltekst i vitenarkiv
• Goodwin, Morten (2024). Robotenes Revolusjon: Sikkerhet eller Sårbarhet? Fulltekst i vitenarkiv

Se alle arbeider i NVA