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Found 10 papers, 4 papers with code
UniGuard: Towards Universal Safety Guardrails for Jailbreak Attacks on Multimodal Large Language Models
Multimodal large language models (MLLMs) have revolutionized vision-language understanding but remain vulnerable to multimodal jailbreak attacks, where adversarial inputs are meticulously crafted to elicit harmful or inappropriate responses.
Adversarial Text Rewriting for Text-aware Recommender Systems
Text-aware recommender systems incorporate rich textual features, such as titles and descriptions, to generate item recommendations for users.
IntentRec: Predicting User Session Intent with Hierarchical Multi-Task Learning
Recommender systems have played a critical role in diverse digital services such as e-commerce, streaming media, social networks, etc.
Cross-Modal Projection in Multimodal LLMs Doesn't Really Project Visual Attributes to Textual Space
It is desirable to understand the roles of these two modules in modeling domain-specific visual attributes to inform the design of future models and streamline the interpretability efforts on the current models.
FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning
Modern recommender systems may output considerably different recommendations due to small perturbations in the training data.
Hierarchical Multi-Task Learning Framework for Session-based Recommendations
However, the H-MTL framework has not been investigated in SBRSs yet.
M2TRec: Metadata-aware Multi-task Transformer for Large-scale and Cold-start free Session-based Recommendations
Using one public and one large industrial dataset, we experimentally show that state-of-the-art SBRSs have low performance on sparse sessions with sparse items.
Implicit Session Contexts for Next-Item Recommendations
Session-based recommender systems capture the short-term interest of a user within a session.
Rank List Sensitivity of Recommender Systems to Interaction Perturbations
We introduce a measure of stability for recommender systems, called Rank List Sensitivity (RLS), which measures how rank lists generated by a given recommender system at test time change as a result of a perturbation in the training data.
Influence-guided Data Augmentation for Neural Tensor Completion
In this paper, we propose DAIN, a general data augmentation framework that enhances the prediction accuracy of neural tensor completion methods.
