Multimodal hate detection aims to identify hate content across multiple modalities for promoting a harmonious online environment. Despite promising progress, three critical challenges, the absence of implicit hateful cues, the cross-modal-induced hate, and the diversity of hate target groups, inherent in the multimodal hate detection task, have been overlooked. To address these challenges, we propose a hypergraph-based prompting fusion model. Our model first uses tailored prompts to infer implicit hateful cues. It then introduces hyperedges to capture cross-modal-induced hate and applies a diversity-oriented hyperedge expansion strategy to account for different hate target groups. Finally, hypergraph convolution fuses diverse hateful cues, enhancing the exploration of cross-modal hate and targeting specific groups. Experimental results on two benchmark datasets show that our model achieves state-of-the-art performance in multimodal hate detection.

The proliferation of structured tabular data in domains like healthcare and finance has intensified the demand for precise table question answering, particularly for complex numerical reasoning and cross-domain generalization. Existing approaches struggle with implicit semantics and multi-step arithmetic operations. This paper presents our solution for SemEval-2025 task,including three synergistic components: (1) a Schema Profiler that extracts structural metadata via LLM-driven analysis and statistical validation, (2) a Hierarchical Chain-of-Thought module that decomposes questions into four stages(semantic anchoring, schema mapping, query synthesis, and self-correction)to ensure SQL validity, and (3) a Confidence-Accuracy Voting mechanism that resolves discrepancies across LLMs through weighted ensemble decisions. Our framework achieves scores of 81.23 on Databench and 81.99 on Databench_lite, ranking 6th and 5th respectively, demonstrating the effectiveness of structured metadata guidance and cross-model deliberation in complex TableQA scenarios.

This paper introduces DUTIR831’s approach to SemEval-2025 Task 5, which focuses on generating relevant subjects from the Integrated Authority File (GND) for tagging multilingual technical records in the TIBKAT database. To address challenges in understanding the hierarchical GND taxonomy and automating subject assignment, a three-stage approach is proposed: (1) a data synthesis stage that utilizes LLM to generate and selectively filter high-quality data, (2) a model training module that leverages LLMs and various training strategies to acquire GND knowledge and refine TIBKAT preferences, and (3) a subject terms completion mechanism consisting of multi-sampling ranking, subject terms extraction using a LLM, vector-based model retrieval, and various re-ranking strategies.The quantitative evaluation results show that our system is ranked 2nd in the all-subject datasets and 4th in the tib-core-subjects datasets. And the qualitative evaluation results show that the system is ranked 2nd in the tib-core-subjects datasets.

The development of social platforms has facilitated the proliferation of disinformation, with memes becoming one of the most popular types of propaganda for disseminating disinformation on the internet. Effectively detecting the persuasion techniques hidden within memes is helpful in understanding user-generated content and further promoting the detection of disinformation on the internet. This paper demonstrates the approach proposed by Team DUTIR938 in Subtask 2b of SemEval-2024 Task 4. We propose a dual-channel model based on semi-supervised learning and model ensemble. We utilize CLIP to extract image features, and employ various pretrained language models under task-adaptive pretraining for text feature extraction. To enhance the detection and generalization capabilities of the model, we implement sample data augmentation using semi-supervised pseudo-labeling methods, introduce adversarial training strategies, and design a two-stage global model ensemble strategy. Our proposed method surpasses the provided baseline method, with Macro/Micro F1 values of 0.80910/0.83667 in the English leaderboard. Our submission ranks 3rd/19 in terms of Macro F1 and 1st/19 in terms of Micro F1.