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Sayan Layek

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2 papers

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TMLR Journal 2025 Journal Article

MemeSense: An Adaptive In-Context Framework for Social Commonsense Driven Meme Moderation

Sayantan Adak
Somnath Banerjee
Rajarshi Mandal
Avik Halder
Sayan Layek
Rima Hazra
Animesh Mukherjee

Online memes are a powerful yet challenging medium for content moderation, often masking harmful intent behind humor, irony, or cultural symbolism. Conventional moderation systems “especially those relying on explicit text” frequently fail to recognize such subtle or implicit harm. We introduce MemeSense, an adaptive framework designed to generate socially grounded interventions for harmful memes by combining visual and textual understanding with curated, semantically aligned examples enriched with commonsense cues. This enables the model to detect nuanced complexed threats like misogyny, stereotyping, or vulgarity “even in memes lacking overt language”. Across multiple benchmark datasets, MemeSense outperforms state-of-the-art methods, achieving up to 35% higher semantic similarity and 9% improvement in BERTScore for non-textual memes, and notable gains for text-rich memes as well. These results highlight MemeSense as a promising step toward safer, more context-aware AI systems for real-world content moderation. The code and data are available at: https://github.com/sayantan11995/MemeSense

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AAAI Conference 2025 Conference Paper

SafeInfer: Context Adaptive Decoding Time Safety Alignment for Large Language Models

Somnath Banerjee
Sayan Layek
Soham Tripathy
Shanu Kumar
Animesh Mukherjee
Rima Hazra

Language models aligned for safety often exhibit fragile and imbalanced mechanisms, increasing the chances of producing unsafe content. In addition, editing techniques to incorporate new knowledge can further compromise safety. To tackle these issues, we propose SafeInfer, a context-adaptive, decoding-time safety alignment strategy for generating safe responses to user queries. safeInfer involves two phases: the 'safety amplification' phase, which uses safe demonstration examples to adjust the model’s hidden states and increase the likelihood of safer outputs, and the 'safety-guided decoding' phase, which influences token selection based on safety-optimized distributions to ensure the generated content adheres to ethical guidelines. Further, we introduce HarmEval, a novel benchmark for comprehensive safety evaluations, designed to address potential misuse scenarios in line with the policies of leading AI technology companies.

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