Are Frontier Large Language Models Suitable for Q&A in Science Centres?

This paper investigates the suitability of frontier Large Language Models (LLMs) for Q&A interactions in science centres, with the aim of boosting visitor engagement while maintaining factual accuracy. Using a dataset of questions collected from the National Space Centre in Leicester (UK), we evaluated responses generated by three leading models: OpenAI's GPT-4, Claude 3.5 Sonnet, and Google Gemini 1.5. Each model was prompted for both standard and creative responses tailored to an 8-year-old audience, and these responses were assessed by space science experts based on accuracy, engagement, clarity, novelty, and deviation from expected answers. The results revealed a trade-off between creativity and accuracy, with Claude outperforming GPT and Gemini in both maintaining clarity and engaging young audiences, even when asked to generate more creative responses. Nonetheless, experts observed that higher novelty was generally associated with reduced factual reliability across all models. This study highlights the potential of LLMs in educational settings, emphasizing the need for careful prompt engineering to balance engagement with scientific rigor.

Do LLMs Agree on the Creativity Evaluation of Alternative Uses?

This paper investigates whether large language models (LLMs) show agreement in assessing creativity in responses to the Alternative Uses Test (AUT). While LLMs are increasingly used to evaluate creative content, previous studies have primarily focused on a single model assessing responses generated by the same model or humans. This paper explores whether LLMs can impartially and accurately evaluate creativity in outputs generated by both themselves and other models. Using an oracle benchmark set of AUT responses, categorized by creativity level (common, creative, and highly creative), we experiment with four state-of-the-art LLMs evaluating these outputs. We test both scoring and ranking methods and employ two evaluation settings (comprehensive and segmented) to examine if LLMs agree on the creativity evaluation of alternative uses. Results reveal high inter-model agreement, with Spearman correlations averaging above 0.7 across models and reaching over 0.77 with respect to the oracle, indicating a high level of agreement and validating the reliability of LLMs in creativity assessment of alternative uses. Notably, models do not favour their own responses, instead they provide similar creativity assessment scores or rankings for alternative uses generated by other models. These findings suggest that LLMs exhibit impartiality and high alignment in creativity evaluation, offering promising implications for their use in automated creativity assessment.

Generating Realistic Synthetic Relational Data through Graph Variational Autoencoders

Synthetic data generation has recently gained widespread attention as a more reliable alternative to traditional data anonymization. The involved methods are originally developed for image synthesis. Hence, their application to the typically tabular and relational datasets from healthcare, finance and other industries is non-trivial. While substantial research has been devoted to the generation of realistic tabular datasets, the study of synthetic relational databases is still in its infancy. In this paper, we combine the variational autoencoder framework with graph neural networks to generate realistic synthetic relational databases. We then apply the obtained method to two publicly available databases in computational experiments. The results indicate that real databases' structures are accurately preserved in the resulting synthetic datasets, even for large datasets with advanced data types.