Conspiracy theories widely spread on social media and can harm society by increasing mistrust, vaccine hesitancy, and political radicalization. However, most automated detection systems have traditionally relied on topic-specific classifiers, which often struggle to generalize across domains and provide little explanation for why a text is considered conspiratorial. To address these limitations, this paper explores various LLMs on the SemEval-2026 Task 10: psycholinguistic conspiracy marker extraction and binary conspiracy detection from Reddit submission statements. Specifically, we adopt a training-free few-shot prompting approach using different instruction-tuned large language models under a variety of few-shot settings (k in {0,1,5,10,15, 20}). Within this framework, the proposed prompting strategy incorporates psychology-informed instructions to guide the models in identifying conspiracy-related signals. As a result, the presented system achieves an F1 score of 0.21 for marker extraction and 0.81 for conspiracy detection, ranking 16th out of 30 teams in Subtask~1 and 36th out of 52 in Subtask~2 without any task-specific fine-tuning. These results suggest that psycholinguistically grounded prompting can support interpretable conspiracy analysis; however, challenges remain in identifying implicit markers.

Generating humorous text is one of the most challenging tasks in natural language generation, as models must simultaneously juggle creativity, cultural understanding, and rules. To tackle these issues, this paper introduces our system for Subtask A of SemEval-2026 Task 1: MWAHAHA - Models Write Automatic Humor And Humans Annotate, which asks for single-sentence jokes with two rules—certain words must be included, and the joke must relate to a news headline—in English, Spanish, and Chinese. Our method uses instruction-tuned language models: Qwen2.5-3B-Instruct for English and Chinese, and Salamandra-2B-Instruct for Spanish, paired with language-specific prompts, special sampling for outputs, and a strong cleaning process after jokes are generated. Without additional task-specific training, our system generates jokes that adhere to the rules in all three languages, demonstrating that simple prompt design and small, instruction-tuned models can be a strong, efficient way to generate funny text across multiple languages.

Detecting misogyny in memes is challenging due to their complex interplay of images and text that often disguise offensive content. Current AI models struggle with these cross-modal relationships and contain inherent biases. We tested multiple approaches for the Misogyny Meme Detection task at LT-EDI@LDK 2025: ChineseBERT, mBERT, and XLM-R for text; DenseNet, ResNet, and InceptionV3 for images. Our best-performing system fused fine-tuned ChineseBERT and DenseNet features, concatenating them before final classification through a fully connected network. This multimodal approach achieved a 0.93035 macro F1-score, winning 1st place in the competition and demonstrating the effectiveness of our strategy for analyzing the subtle ways misogyny manifests in visual-textual content.