Large Language Models

Enhancing the Evaluation of Fault Detection Models in Smart Agriculture Using LLM Agents for Rule-Based Anomaly Generation

In the context of Agriculture 4.0, advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics play a critical role in enhancing the efficiency and sustainability of farming operations. These …

Are Large Language Models Really Bias-Free? Jailbreak Prompts for Assessing Adversarial Robustness to Bias Elicitation

Large Language Models (LLMs) have revolutionized artificial intelligence, demonstrating remarkable computational power and linguistic capabilities. However, these models are inherently prone to various biases stemming from their training data. These …

Are Large Language Models Really Bias-Free? Jailbreak Prompts for Assessing Adversarial Robustness to Bias Elicitation

Large Language Models (LLMs) have revolutionized artificial intelligence, demonstrating remarkable computational power and linguistic capabilities. However, these models are inherently prone to various biases stemming from their training data. These …

Multi-dimensional Classification on Social Media Data for Detailed Reporting with Large Language Models

Every day, more and more people harness the power of social media platforms to express their thoughts, share information and personal experiences, and engage with others. All this knowledge can then be transformed into informative reports with the …

XAI-driven Knowledge Distillation of Large Language Models for Efficient Deployment on Low-Resource Devices

Large Language Models (LLMs) are characterized by their inherent memory inefficiency and compute-intensive nature, making them impractical to run on low-resource devices and hindering their applicability in edge AI contexts. To address this issue, …

Detecting mental disorder on social media: a ChatGPT-augmented explainable approach

In the digital era, the prevalence of depressive symptoms expressed on social media has raised serious concerns, necessitating advanced methodologies for timely detection. This paper addresses the challenge of interpretable depression detection by …