--- dataset_info: features: - name: image dtype: image - name: Text dtype: string - name: Label dtype: string - name: Paradigm dtype: string - name: Incident_based dtype: string - name: Humor dtype: string - name: Metaphor dtype: string - name: Metaphor Object dtype: string - name: Metaphor Explanation dtype: string - name: Misogynistic intensity dtype: string splits: - name: train num_bytes: 179710931.0 num_examples: 1500 - name: test num_bytes: 60020368.0 num_examples: 500 download_size: 242960052 dataset_size: 239731299.0 configs: - config_name: default data_files: - split: train path: data/train-* - split: test path: data/test-* --- # BANMIME:Misogyny Detection with Metaphor Explanation on Bangla Memes [![anthology](https://img.shields.io/badge/ACL%20Anthology-EMNLP%202025-EE161F?logo=data:image/png;base64,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&style=flat")](https://aclanthology.org/) [![code](https://img.shields.io/badge/Code-Ayon128/BANMIME-blue?logo=GitHub)](https://github.com/Ayon128/BANMIME) [![Kaggle](https://img.shields.io/badge/Kaggle-Dataset-20BEFF?logo=kaggle&logoColor=white)](https://www.kaggle.com/datasets/mdayonmia1804128/misogyny-meme-dataset) [Md. Ayon Mia*](https://github.com/Ayon128), [AKM Moshiur Rahman*](https://huggingface.co/pltops), [Khadiza Sultana Sayma*](https://github.com/Khadiza13), [Md Fahim*](https://github.com/md-fahim/), [Md Tahmid Hasan Fuad](), [Muhammad Ibrahim Khan](), and [AKM Mahbubur Rahman](). ## Dataset Overview - Misogyny detection (binary) and category identification (multi-class) dataset on 2,000 Bangla meme samples. - Four misogyny categories: Stereotype (30.6%), Objectification (25.9%), Shaming (38.0%), and Violence (5.4%). - Sourced from 3 social media platforms: Facebook (1,300), Instagram (450), and Reddit (250). - Each sample includes misogyny labels, humor types, metaphor localization, and detailed human-written explanations. - High inter-annotator agreement with Cohen's Îș = 0.74. ## Data Format | **Column Title** | **Description** | |-------------------------|---------------------------------------------------------------------------------| | `meme_id` | Unique identifier for each meme entry. | | `image_path` | Path to the meme image file. | | `text_content` | Extracted text content from the meme. | | `misogyny_label` | Primary misogyny category (Stereotype / Objectification / Shaming / Violence). | | `humor_type` | Type of humor used (Mockery / Sarcastic / Ironic / Satirical / Other). | | `metaphor_localization`| Location of metaphor (Text / Image / Both). | | `metaphor_object` | Object used in metaphorical expression. | | `misogyny_intensity` | Intensity level (High / Moderate / Low). | | `meme_template` | Template category (Troll Face / Wojak / Doge / etc). | | `explanation` | Detailed human-written explanation of misogynistic content. | ## 🚀 Getting Started To run the code, please check the scripts in the `Misogyny_Code/Evaluate` and `Misogyny_Code/LLaMA-Factory` folders. --- ## 📊 Vision-Language Model Performance We evaluate baselines using both open-source and closed-source vision-language models across different prompting strategies and fine-tuning approaches. ### Zero-Shot Prompt | Models | Sham | Stereo | Obj | Vio | Avg | BScore | LAVE | Avg Expl | |------------------------|-------|--------|-------|-------|-------|--------|-------|----------| | **Closed Source VLMs** | | | | | | | | | | Gemini2.0 Flash | 34.97 | 52.55 | 65.77 | 72.41 | 56.43 | 86.70 | 35.00 | 60.85 | | GPT-4o-mini | 26.26 | 61.33 | 60.33 | 77.42 | 56.34 | 87.26 | 27.20 | 57.23 | | **Open Source VLMs** | | | | | | | | | | Llama-3.2V 11B | 12.31 | 18.12 | 42.50 | 16.13 | 22.27 | 82.82 | 0.35 | 41.59 | | Gemma-3-12B | 52.02 | 22.67 | 48.76 | 67.74 | 47.80 | 83.54 | 8.00 | 45.77 | | Qwen2.5-VL 7B | 22.73 | 41.33 | 57.02 | 35.48 | 39.14 | 78.60 | 0.60 | 39.60 | | Phi-3.5 | 23.23 | 67.33 | 25.62 | 6.45 | 30.66 | 82.30 | 0.23 | 41.27 | | Phi-4 | 49.49 | 28.86 | 20.83 | 16.13 | 28.83 | 81.63 | 0.20 | 40.92 | | LLaVA-1.5 7B | 16.92 | 39.60 | 27.50 | 19.35 | 25.84 | 82.40 | 0.22 | 41.31 | | LLaVA-NeXT 7B | 29.59 | 25.68 | 16.53 | 16.67 | 22.12 | 79.25 | 0.45 | 39.85 | ### CoT Prompt | Models | Sham | Stereo | Obj | Vio | Avg | BScore | LAVE | Avg Expl | |------------------------|-------|--------|-------|-------|-------|--------|-------|----------| | **Closed Source VLMs** | | | | | | | | | | Gemini2.0 Flash | 48.11 | 64.23 | 47.75 | 55.17 | 53.82 | 86.96 | 31.60 | 59.28 | | GPT-4o-mini | 22.54 | 80.69 | 44.55 | 33.33 | 45.28 | 87.20 | 22.94 | 55.07 | | **Open Source VLMs** | | | | | | | | | | Llama-3.2V 11B | 11.68 | 35.33 | 16.67 | 3.33 | 16.75 | 83.53 | 1.73 | 42.63 | | Gemma-3-12B | 75.25 | 11.33 | 38.02 | 41.94 | 41.64 | 84.49 | 7.36 | 45.93 | | Qwen2.5-VL 7B | 24.24 | 61.74 | 28.33 | 16.13 | 32.61 | 84.56 | 5.13 | 44.85 | | Phi-4 | 10.71 | 59.46 | 14.17 | 2.45 | 22.09 | 84.41 | 1.29 | 42.85 | | LLaVA-1.5 7B | 7.61 | 57.72 | 10.08 | 6.45 | 20.47 | 84.34 | 0.27 | 42.31 | | LLaVA-NeXT 7B | 8.67 | 43.62 | 11.57 | 6.45 | 17.58 | 84.23 | 2.16 | 43.20 | ### LoRA (CoT) Fine-Tuning | Models | Sham | Stereo | Obj | Vio | Avg | BScore | LAVE | Avg Expl | |----------------|-------|--------|-------|-------|-------|--------|-------|----------| | Llama-3.2V 11B | 32.83 | 65.33 | 52.89 | 48.39 | 49.86 | 86.01 | 8.00 | 47.01 | | Gemma-3-12B | 16.16 | 76.67 | 49.59 | 64.52 | 51.74 | 86.16 | 13.80 | 49.98 | | Qwen2.5-VL 7B | 44.95 | 59.33 | 48.76 | 51.61 | 51.16 | 85.36 | 5.00 | 45.18 | | LLaVA-1.5 7B | 24.62 | 48.33 | 52.98 | 54.26 | 45.05 | 85.19 | 4.80 | 44.20 | | Paligemma-2-10B| 8.08 | 61.33 | 42.98 | 61.29 | 43.42 | 81.80 | 2.00 | 41.90 | --- ## 🔧 Fine-Tuning Strategies We propose three distinct fine-tuning approaches: - **Standard LoRA Fine-Tuning**: Basic fine-tuning with LoRA adaptation - **Augmented Direct Prompt Fine-Tuning**: Enhanced training with data augmentation - **Chain-of-Thought Fine-Tuning**: Structured reasoning supervision for improved metaphor understanding ### Comparative Performance | Models | Sham | Stereo | Obj | Vio | Avg | |---------------------|-------|--------|-------|-------|-------| | **LLaMa-3.2V 11B** | | | | | | | LoRAaug | 0.5 | 99.3 | 0.0 | 3.2 | 25.9 | | LoRAstd | 11.6 | 61.3 | 49.6 | 61.3 | 46.0 | | LoRACoT | 32.8 | 65.3 | 52.9 | 48.4 | 49.9 | | **Gemma-3 12B** | | | | | | | LoRAaug | 8.1 | 98.0 | 7.4 | 9.7 | 30.8 | | LoRAstd | 10.1 | 70.7 | 49.6 | 48.4 | 44.7 | | LoRACoT | 16.2 | 76.7 | 49.6 | 64.5 | 51.7 | | **Qwen2.5-VL 7B** | | | | | | | LoRAaug | 49.0 | 58.7 | 5.8 | 22.6 | 34.0 | | LoRAstd | 47.0 | 59.0 | 27.3 | 37.1 | 42.6 | | LoRACoT | 45.0 | 59.3 | 48.8 | 51.6 | 51.2 | | **LLaVa-1.5 7B** | | | | | | | LoRAaug | 13.1 | 84.0 | 0.0 | 3.2 | 25.1 | | LoRAstd | 8.1 | 46.7 | 38.8 | 48.4 | 35.5 | | LoRACoT | 24.6 | 48.3 | 53.0 | 54.3 | 45.1 | | **Paligemma-2 10B** | | | | | | | LoRAaug | 38.9 | 18.0 | 24.8 | 16.1 | 24.4 | | LoRAstd | 9.6 | 39.3 | 39.7 | 58.1 | 36.7 | | LoRACoT | 8.1 | 61.3 | 43.0 | 61.3 | 43.4 | ## 🔑 Key Findings - **Chain-of-Thought prompting and fine-tuning** improves both classification and explanation quality. - **Closed-source models** outperform open-source models in both classification and explanation tasks. - **Metaphor interpretation** remains challenging for current VLMs, especially in cultural contexts - **Open-source VLMs** struggle with visual metaphors, with performance dropping further when generating explanations.