Publications
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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),
p. 21–41.
doi:
10.5121/ijci.2025.140202.
Full text in Research Archive
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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.
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Voigt, Jakob Michael; Grundetjern, Morten; Andersen, Per-Arne & Goodwin, Morten
(2025).
DESS: Dimensional-PDFs for Embedding Space Sampling.
Lecture Notes in Computer Science (LNCS).
ISSN 0302-9743.
16301,
p. 3–17.
doi:
10.1007/978-3-032-11402-0_1.
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Probabilistic sampling in machine learning models is a technique for introducing beneficial randomness and variability during output generation. It enhances output diversity and creativity, particularly in tasks where slight variations can lead to novel and interesting results. Large Language Models (LLMs), for instance, use this technique to generate varied, contextually appropriate responses that mirror the flexibility of human language. This paper presents a novel method for training a neural network to output the defining parameters of one Probability Density Function (PDF) for each dimension in an embedding space. Our approach, Dimensional-PDFs for Embedding Space Sampling (DESS), leverages these PDFs at inference time to sample from an embedding space, offering a new approach to probabilistic sampling in model outputs. Our method employs simple loss functions and a multi-step loss computation to learn both mean and variance parameters without requiring sampling during training, making it computationally efficient and easy to integrate with existing architectures. We validate DESS through controlled experiments on synthetic datasets, demonstrating accurate approximations of ground-truth distribution parameters across varying Gaussian target distributions. The framework has potential applications in large-vocabulary models where it could enable significant parameter reductions while maintaining calibrated uncertainty estimates. We present DESS using normal distributions, though the approach can be extended to other distribution types for specific sampling requirements.
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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.
In Bramer, Max & Stahl, Frederic (Ed.),
Artificial Intelligence XLI: 44th SGAI International Conference on Artificial Intelligence, AI 2024.
Springer.
ISSN 9783031779145.
p. 3–15.
doi:
10.1007/978-3-031-77915-2_1.
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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.
View all works in NVA
Published
Apr. 16, 2024 11:13 AM