arXiv AI

UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes

The paper introduces UkrMeme, a dataset of 200 Ukrainian wartime memes annotated for persuasive techniques, and PTM-UA, a Ukrainian translation of a subset of the Propaganda 950 benchmark that retains the original visual content. The authors evaluate vision‑language models in a zero‑shot prompting setup across image, text, and multimodal inputs using hierarchical F1 metrics. They also compare Layer‑wise Relevance Propagation and model‑generated token attributions to analyze the semantic categories of evidence emphasized by each explanation method.

arXiv Computer Vision

5d ago

Can MLLMs Reason About Visual Persuasion? Evaluating the Efficacy and Faithfulness of Reasoning

The paper investigates whether Multimodal Large Language Models (MLLMs) can reason about visual persuasion. It finds that current models often shortcut by citing obvious visual elements instead of truly reasoning about diverse cues, and proposes a fine‑tuning method using multi‑perspective rationales along with an evaluation framework that separates prediction performance from rationale faithfulness. The study shows that while fine‑tuning improves prediction, higher accuracy does not guarantee more faithful reasoning, underscoring the need for distinct evaluation of effectiveness and faithfulness.

By Naeun Lee, Hyunjong Kim, Sunghwan Choi, Injin Kong, Yohan Jo

llms fine-tuning multimodal

arXiv AI

Jul 28

Token-Region Guided Cross-Attention Fusion for Multimodal Affect Interpretation

arXiv:2607. 23493v1 Announce Type: cross Abstract: Automated analysis of multimodal content on social networks has become a critical task for understanding public sentiment and information diffusion in the digital age.

By Musa Tur Farazi, Nufayer Jahan Reza

llms diffusion computer-vision multimodal benchmarks safety

Hugging Face Trending Papers

Jul 29

AHA-Memes: A Fine-Grained Multimodal Benchmark for Understanding Hate in Arabic Memes

Hateful memes are a growing form of multimodal online harm, where hostile intent is often conveyed through the joint interpretation of images, text, cultural references, and implicit targets. While hateful meme detection has advanced in high-resource languages, Arabic remains underexplored, with existing meme resources focusing mainly on propaganda or coarse harmful-content labels.

llms fine-tuning multimodal benchmarks

arXiv AI

Jul 20

Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes

arXiv:2607. 15442v1 Announce Type: new Abstract: Internet memes intertwine visual cues, textual content, and cultural context, making them particularly challenging to interpret in scenarios where humor, sarcasm, and harmful intent coexist.

By Shanhong Liu, Pai Chet Ng, De Wen Soh, Malika Meghjani, Konstantinos N. Plataniotis

llms multimodal benchmarks safety

arXiv Computation and Language

Oct 5

Unmasking Propaganda: A Comparative Analysis of Masked and Causal Language Models

arXiv:2610.03077v1 Announce Type: new Abstract: Propaganda detection is an essential task in natural language processing (NLP), particularly in the context of manipulative political communications. H...

By Claudiu Creanga, Ioachim Lihor, Liviu P. Dinu

llms nlp fine-tuning benchmarks

arXiv AI

Aug 28

Beyond Accuracy: A Qualitative Analysis of Vision-Language Models for Hate Speech Detection in Memes

The paper examines how four leading vision‑language models—LLaVA‑7B, Qwen‑VL, GPT‑4o mini, and Claude 3 Haiku—perform in detecting hateful content within memes. It evaluates the models under zero‑shot and few‑shot prompting, focusing not only on classification accuracy but also on the qualitative justifications they generate. The study highlights that these models often overlook contextual nuances, irony, and subtle cues essential for accurately identifying hate speech in memes.

By Muhammad Jawad Chowdhury, Adiba Hasan, Ishrak Hossain, Shahriar Ivan, Sabbir Ahmed

llms multimodal benchmarks

arXiv AI

Jun 16

Adapting Reinforcement Learning with Chain-of-Thought Supervision for Explainable Detection of Hateful and Propagandistic Memes

arXiv:2606. 15307v1 Announce Type: cross Abstract: Hateful and propagandistic memes exploit the interplay between images and text to convey harmful intent that neither modality reveals alone.

By Mohamed Bayan Kmainasi, Mucahid Kutlu, Ali Ezzat Shahroor, Abul Hasnat, Firoj Alam

llms reinforcement-learning efficiency multimodal benchmarks

Hugging Face Trending Papers

Oct 2

Unmasking Propaganda: A Comparative Analysis of Masked and Causal Language Models

Propaganda detection is an essential task in natural language processing (NLP), particularly in the context of manipulative political communications. However, identifying specific propaganda technique...

llms nlp fine-tuning benchmarks

arXiv Machine Learning

Sep 24

Small Cues, Big Consequences: Learning Pivotal Cues for Multimodal Meme Classification

arXiv:2609.26907v1 Announce Type: cross Abstract: Memes often derive their harmful, hateful, or sarcastic meaning from small but decisive visual, textual, or cross-modal cues. Existing multimodal cla...

By Akshit Sharma, Prashant W. Patil

multimodal benchmarks

arXiv AI

Aug 25

From Recognition to Reasoning: Advancing Multimodal Harmful Meme Detection via Chain-of-Thought Alignment

The paper introduces MemeMind, a large-scale dataset for detecting harmful memes that includes a detailed taxonomy and Chain-of-Thought reasoning annotations. It also proposes MemeGuard, a multimodal framework that uses a three-stage training strategy to improve visual understanding, reasoning, and discrimination of harmful content. Experiments show MemeGuard surpasses current state-of-the-art methods on MemeMind, advancing detection accuracy and interpretability.

By Hexiang Gu, Qifan Yu, Yuan Liu, Zikang Li, Saihui Hou, Jian Zhao, Zhaofeng He

multimodal benchmarks safety

arXiv Computation and Language

6d ago

BanglaRhet: Benchmarking Classical and Transformer Models for Rhetorical and Persuasion Detection in Bangla Political Speech

arXiv:2610.09464v1 Announce Type: new Abstract: Political discourse often uses rhetorical and persuasive language to frame narratives, influence public opinion, and mobilize audiences. While Bangla n...

By Rohit Kumar Sen, Anik Chowdhury

llms nlp benchmarks

arXiv AI

Sep 21

MemeLens: Multilingual Multitask VLMs for Memes

MemeLens is a unified multilingual, multitask Vision‑Language Model designed to improve meme understanding across a wide range of tasks such as hate, misogyny, propaganda, sentiment, and humour. The authors consolidated 38 public meme datasets, mapping their labels into a shared taxonomy of 20 tasks covering harm, targets, figurative intent, and affect, and conducted extensive experiments to show that multimodal training and a unified approach outperform fine‑tuning on individual datasets. All experimental resources, the model, and the datasets are released publicly for community use.

By Ali Ezzat Shahroor, Mohamed Bayan Kmainasi, Abul Hasnat, Dimitar Dimitrov, Giovanni Da San Martino, Preslav Nakov, Firoj Alam

llms robotics fine-tuning multimodal

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