Poetry generation in low-resource languages such as Bangla is particularly challenging due to the scarcity of structured poetic corpora and the complexity of its metrical system (matra). We present a structure-aware framework for Bangla poetry generation using pretrained Bangla large language models (LLMs)–TigerLLM, TituLLM, and BanglaT5–trained on general non-poetic text corpora augmented with rich structural control tokens. These tokens capture rhyme, meter, word count, and line boundaries, enabling unsupervised modeling of poetic form without curated poetry datasets. Unlike prior fixed-pattern approaches, our framework introduces variable control compositions, allowing models to generate flexible poetic structures. Experiments show that explicit structural conditioning improves rhyme consistency and metrical balance while maintaining semantic coherence. Our study provides the first systematic evaluation of Bangla LLMs for form-constrained creative generation, offering insights into structural representation in low-resource poetic modeling.

Emotion intensity prediction in text enhances conversational AI by enabling a deeper understanding of nuanced human emotions, a crucial yet underexplored aspect of natural language processing (NLP). This study employs Transformer-based models to classify emotion intensity levels (0–3) for five emotions: anger, fear, joy, sadness, and surprise. The dataset, sourced from the SemEval shared task, was preprocessed to address class imbalance, and model training was performed using fine-tuned *bert-base-uncased*. Evaluation metrics showed that *sadness* achieved the highest accuracy (0.8017) and F1-macro (0.5916), while *fear* had the lowest accuracy (0.5690) despite a competitive F1-macro (0.5207). The results demonstrate the potential of Transformer-based models in emotion intensity prediction while highlighting the need for further improvements in class balancing and contextual representation.