This research systematically forms an impression of the capabilities of advanced language models in addressing the BRAINTEASER task introduced at SemEval 2024, which is specifically designed to explore the models’ proficiency in lateral commonsense reasoning. The task sets forth an array of Sentence and Word Puzzles, carefully crafted to challenge the models with scenarios requiring unconventional thought processes. Our methodology encompasses a holistic approach, incorporating pre-processing of data, fine-tuning of transformer-based language models, and strategic data augmentation to explore the depth and flexibility of each model’s understanding. The preliminary results of our analysis are encouraging, highlighting significant potential for advancements in the models’ ability to engage in lateral reasoning. Further insights gained from post-competition evaluations suggest scopes for notable enhancements in model performance, emphasizing the continuous evolution of the models in mastering complex reasoning tasks.

This paper describes our participation in Task1 (VITD) of BLP Workshop 1 at EMNLP 2023,focused on the detection and categorizationof threats linked to violence, which could po-tentially encourage more violent actions. Ourapproach involves fine-tuning of pre-trainedtransformer models and employing techniqueslike self-training with external data, data aug-mentation through back-translation, and en-semble learning (bagging and majority voting).Notably, self-training improves performancewhen applied to data from external source butnot when applied to the test-set. Our anal-ysis highlights the effectiveness of ensemblemethods and data augmentation techniques inBangla Text Classification. Our system ini-tially scored 0.70450 and ranked 19th amongthe participants but post-competition experi-ments boosted our score to 0.72740.