Papers by Prince Jha
Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations (2024.eacl-long)
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| Challenge: | Recent laws like “right to explanations” have spurred research in developing interpretable models . a recent study has shown that multimodal explanations improve performance in generating textual justifications . |
| Approach: | They propose to use visual and textual modalities to explain why a given meme is cyberbullying . they use a Contrastive Language-Image Pretraining approach to generate textual justifications . |
| Outcome: | The proposed model improves performance in visual and textual explanations and identifies the visual evidence supporting a decision. |
Peeking inside the black box: A Commonsense-aware Generative Framework for Explainable Complaint Detection (2023.acl-long)
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| Challenge: | Complaining is an expression of negative emotions communicated due to a discrepancy between reality and expectations. |
| Approach: | They propose to use an explainable complaint dataset to generate a commonsense-aware generative framework that can predict the complaint cause, severity level, emotion, and polarity of the text. |
| Outcome: | The proposed model can predict the complaint cause, severity level, emotion, and polarity of the text in addition to detecting whether it is a complaint or not. |
MemeGuard: An LLM and VLM-based Framework for Advancing Content Moderation via Meme Intervention (2024.acl-long)
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| Challenge: | Existing studies on content moderation of toxic memes focus on text-based content . current research neglects the widespread influence of multimodal content like memes . |
| Approach: | They propose a framework leveraging Large Language Models and Visual Language Model (VLMs) for meme intervention. |
| Outcome: | The proposed framework enables users to generate relevant and effective responses to toxic memes. |
On the Cultural Anachronism and Temporal Reasoning in Vision Language Models (2026.findings-acl)
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| Challenge: | Vision-Language Models (VLMs) are increasingly applied to cultural heritage materials. |
| Approach: | They propose a temporal anachronism benchmark to evaluate temporal reasoning on 1,600 Indian cultural artifacts. |
| Outcome: | The proposed model achieves only 58.7% accuracy on the best model, which is a significant performance gap across architectures and scales. |
GenEx: A Commonsense-aware Unified Generative Framework for Explainable Cyberbullying Detection (2023.emnlp-main)
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| Challenge: | a significant gap exists in understanding code-mixed languages and the need for explainability in this context. |
| Approach: | They propose to annotate posts with four labels to identify bullies in code-mixed languages . they propose to use a generative framework to reimagine the multitask problem as a text-to-text generation task. |
| Outcome: | The proposed model outperforms baseline models and state-of-the-art models on the BullyExplain dataset. |