Context-Emotion Aware Therapeutic Dialogue Generation: A Multi-component Reinforcement Learning Approach to Language Models for Mental Health Support

The paper introduces AdamHuberDecay, a new adaptive optimizer designed for pre-training large transformer-based generative models. This optimizer replaces the traditional $ ext{l}_2$ penalty used in AdamW with a decoupled smooth Huber regularizer. The proposed method allows for quadratic decay of parameters below a certain threshold while applying linear decay for larger values. This approach aims to improve regularization gradients, maintain invariance to second-moment rescaling, and enhance sparsity for overgrown weights.

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…