--We introduce a dataset for classifying wellness dimensions in social media user posts, covering six key aspects: physical, emotional, social, intellectual, spiritual, and vocational. The dataset is designed to capture these dimensions in user-generated content, with a comprehensive annotation framework developed under the guidance of domain experts. This framework also includes labeling text spans from these posts to provide explanations that highlight the corresponding wellness aspects. We evaluate both traditional machine learning models and advanced transformer-based models for this multi-class classification task, with performance assessed using precision, recall, and F1-score, averaged over 10-fold cross-validation. Post-hoc explanations are applied to ensure the transparency and interpretability of model decisions. The proposed dataset contributes to region-specific wellness assessments in social media and paves the way for personalized well-being evaluations and early intervention strategies in mental health. Mental health disorders have become a critical global health problem, with more than a billion people worldwide affected by mental, neurological, and substance use disorders [1].

Large Language Models (LLMs) play a vital role in applications like conversational agents and content creation, where controlling a model's personality is crucial for maintaining tone, consistency, and engagement. However, traditional prompt-based techniques for controlling personality often fall short, as they do not effectively mitigate the model's inherent biases. In this paper, we introduce a novel method PALETTE that enhances personality control through knowledge editing. By generating adjustment queries inspired by psychological assessments, our approach systematically adjusts responses to personality-related queries similar to modifying factual knowledge, thereby achieving controlled shifts in personality traits. Experimental results from both automatic and human evaluations demonstrate that our method enables more stable and well-balanced personality control in LLMs.

Four-fifths (80%) of small and medium-sized enterprises in Scotland expect to employ robots or other artificial intelligence (AI) by 2035, study findings show. Nearly half (49%) believe they will be reliant on renewable energy sources to power this advancement in technology. And three-quarters (75%) say improving eco-friendliness will help their profitability and make them more attractive to investors. When asked what roles robots would have, 36% of SMEs expect them to be used for tidying the workplace. Carrying out hazardous tasks (39%) and entertainment (46%) were also cited.