Critical Question Generation (CQs-Gen) improves reasoning and critical thinking skills through Critical Questions (CQs), which identify reasoning gaps and address misinformation in NLP, especially as LLM-based chat systems are widely used for learning and may encourage superficial learning habits. The Shared Task on Critical Question Generation, hosted at the 12th Workshop on Argument Mining and co-located in ACL 2025, has aimed to address these challenges. This study proposes a CQs-Gen pipeline using Llama-3-8B-Instruct-GGUF-Q8_0 with few-shot learning, integrating text simplification, NER, and argument schemes to enhance question quality. Through an extensive experiment testing without training, fine-tuning with PEFT using LoRA on 10% of the dataset, and few-shot fine-tuning (using five examples) with an 8-bit quantized model, we demonstrate that the few-shot approach outperforms others. On the validation set, 397 out of 558 generated CQs were classified as Useful, representing 71.1% of the total. In contrast, on the test set, 49 out of 102 generated CQs, accounting for 48% of the total, were classified as Useful following evaluation through semantic similarity and manual assessments.

Identification of hate speech in images with text is a complicated task in the scope of online content moderation, especially when such talk penetrates into the spheres of humor and critical societal topics. This paper deals with Subtask A of the Shared Task on Multimodal Hate, Humor, and Stance Detection in Marginalized Movement@CASE2025. This task is binary classification over whether or not hate speech exists in image contents, and it advances as Hate versus No Hate. To meet this goal, we present a new multimodal architecture that blends the textual and visual features to reach effective classification. In the textual aspect, we have fine-tuned two state-of-the-art transformer models, which are RoBERTa and HateBERT, to extract linguistic clues of hate speech. The image encoder contains both the EfficientNetB7 and a Vision Transformer (ViT) model, which were found to work well in retrieving image-related details. The predictions made by each modality are then merged through an ensemble mechanism, with the last estimate being a weighted average of the text- and image-based scores. The resulting model produces a desirable F1- score metric of 0.7868, which is ranked 10 among the total number of systems, thus becoming a clear indicator of the success of multimodal combination in addressing the complex issue of self-identifying the hate speech in text-embedded images.