AdamHD: Decoupled Huber Decay Regularization for Language Model Pre-Training

The article discusses the evolution of AI technology from GPT-3 to Gemini 3, highlighting the advancements made in artificial intelligence over three years. Gemini 3, developed by Google, is positioned as a more intelligent and factually accurate model compared to its predecessors. The transition from chatbots to more sophisticated AI agents reflects a significant shift in the capabilities of AI systems, aiming to enhance user interactions and provide more reliable information.

Mental health issues pose a significant global socioeconomic challenge, worsened by COVID-19, which has increased the demand for telehealth services. While large language models (LLMs) like GPT-2 provide potential solutions through constant availability and non-judgmental interactions, they often lack the necessary contextual and emotional awareness for effective therapeutic dialogue. This study explores the use of supervised fine-tuning and reinforcement learning to improve GPT-2's ability to generate therapeutic conversations by processing contextual information and emotional states simultan…

This paper presents a transformer-based approach for classifying hope expressions in text. It compares three architectures: BERT, GPT-2, and DeBERTa, for binary classification (Hope vs. Not Hope) and multiclass categorization (five hope-related categories). The initial BERT implementation achieved 83.65% binary and 74.87% multiclass accuracy. BERT outperformed others in extended comparisons, requiring fewer resources. GPT-2 had the lowest accuracy, while DeBERTa showed moderate results but at a higher computational cost. Error analysis highlighted architecture-specific strengths.

Monitoring the spatiotemporal dynamics of forest CO2 uptake, known as Gross Primary Production (GPP), poses significant challenges in terrestrial ecosystem research. While Eddy Covariance towers provide high-frequency estimates, their spatial limitations hinder large-scale assessments. Remote sensing offers a scalable alternative, yet many methods rely on single-sensor spectral indices and statistical models that struggle to capture GPP's complex temporal dynamics. This study evaluates the performance of GPT-2, a transformer model, against LSTM, a recurrent neural network, for GPP prediction u…