From Chat to Checkup: Can Large Language Models Assist in Diabetes Prediction?

While Machine Learning (ML) and Deep Learning (DL) models have been widely used for diabetes prediction, the use of Large Language Models (LLMs) for structured numerical data is still not well explored. In this study, we test the effectiveness of LLMs in predicting diabetes using zero-shot, one-shot, and three-shot prompting methods. We conduct an empirical analysis using the Pima Indian Diabetes Database (PIDD). We evaluate six LLMs, including four open-source models: Gemma-2-27B, Mistral-7B, Llama-3.1-8B, and Llama-3.2-2B. We also test two proprietary models: GPT-4o and Gemini Flash 2.0. In addition, we compare their performance with three traditional machine learning models: Random Forest, Logistic Regression, and Support Vector Machine (SVM). We use accuracy, precision, recall, and F1-score as evaluation metrics. Our results show that proprietary LLMs perform better than open-source ones, with GPT-4o and Gemma-2-27B achieving the highest accuracy in few-shot settings. Notably, Gemma-2-27B also outperforms the traditional ML models in terms of F1-score. However, there are still issues such as performance variation across prompting strategies and the need for domain-specific fine-tuning. This study shows that LLMs can be useful for medical prediction tasks and encourages future work on prompt engineering and hybrid approaches to improve healthcare predictions.

Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs

A large number of studies rely on closed-style multiple-choice surveys to evaluate cultural alignment in Large Language Models (LLMs). In this work, we challenge this constrained evaluation paradigm and explore more realistic, unconstrained approaches. Using the World Values Survey (WVS) and Hofstede Cultural Dimensions as case studies, we demonstrate that LLMs exhibit stronger cultural alignment in less constrained settings, where responses are not forced. Additionally, we show that even minor changes, such as reordering survey choices, lead to inconsistent outputs, exposing the limitations of closed-style evaluations. Our findings advocate for more robust and flexible evaluation frameworks that focus on specific cultural proxies, encouraging more nuanced and accurate assessments of cultural alignment in LLMs.

Religious Bias Landscape in Language and Text-to-Image Models: Analysis, Detection, and Debiasing Strategies

Note: This paper includes examples of potentially offensive content related to religious bias, presented solely for academic purposes. The widespread adoption of language models highlights the need for critical examinations of their inherent biases, particularly concerning religion. This study systematically investigates religious bias in both language models and text-to-image generation models, analyzing both open-source and closed-source systems. We construct approximately 400 unique, naturally occurring prompts to probe language models for religious bias across diverse tasks, including mask filling, prompt completion, and image generation. Our experiments reveal concerning instances of underlying stereotypes and biases associated disproportionately with certain religions. Additionally, we explore cross-domain biases, examining how religious bias intersects with demographic factors such as gender, age, and nationality. This study further evaluates the effectiveness of targeted debiasing techniques by employing corrective prompts designed to mitigate the identified biases. Our findings demonstrate that language models continue to exhibit significant biases in both text and image generation tasks, emphasizing the urgent need to develop fairer language models to achieve global acceptability.