Summary of M3hop-cot: Misogynous Meme Identification with Multimodal Multi-hop Chain-of-thought, by Gitanjali Kumari et al.
M3Hop-CoT: Misogynous Meme Identification with Multimodal Multi-hop Chain-of-Thought
by Gitanjali Kumari, Kirtan Jain, Asif Ekbal
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed M3Hop-CoT framework combines a CLIP-based classifier with a multimodal CoT module to identify misogynous memes on social media. By employing a three-step multimodal prompting principle that induces emotions, target awareness, and contextual knowledge, the model demonstrates strong performance in the macro-F1 score on the SemEval-2022 Task 5 (MAMI task) dataset. The framework’s effectiveness is further validated through evaluations on various benchmark meme datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to identify hate speech against women on social media. They used a special type of AI model that can understand both text and images, and combined it with a technique called “chain-of-thought” prompting. This helps the model to understand the emotions and context behind the memes. The team tested their approach on several datasets and found that it was very effective in identifying misogynous content. |
Keywords
» Artificial intelligence » F1 score » Prompting
