Liang Lin
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The paper introduces ProjLens, an interpretability framework that reveals that backdoor vulnerabilities in Multimodal Large Language Models (MLLMs) are encoded within a low-rank subspace of the projector, causing a measurable semantic shift in poisoned inputs.
This paper introduces a new benchmark to test Tool Description Poisoning (TDP) attacks on LLM agents, demonstrating that even advanced models like GPT-4o are highly vulnerable and that current defenses are often ineffective.
This paper proposes HyCoRec, a method to alleviate the Matthew effect in conversational recommendation by learning multi-aspect preferences.
Papers
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
Yongsen Zheng, Ruilin Xu, Ziliang Chen, Guohua Wang +3 more
This paper proposes HyCoRec, a method to alleviate the Matthew effect in conversational recommendation by learning multi-aspect preferences.