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Found 6 papers, 2 papers with code
Exploring the Privacy Protection Capabilities of Chinese Large Language Models
Large language models (LLMs), renowned for their impressive capabilities in various tasks, have significantly advanced artificial intelligence.
How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendation
Through multiple experiments on two widely-used datasets in the field of conversational recommendation, we highlight several issues with the current evaluation methods for user simulators based on LLMs: (1) Data leakage, which occurs in conversational history and the user simulator's replies, results in inflated evaluation results.
Non-generative Generalized Zero-shot Learning via Task-correlated Disentanglement and Controllable Samples Synthesis
Synthesizing pseudo samples is currently the most effective way to solve the Generalized Zero-Shot Learning (GZSL) problem.
Learning to Learn a Cold-start Sequential Recommender
The cold-start recommendation is an urgent problem in contemporary online applications.
Knowledge Graph-enhanced Sampling for Conversational Recommender System
Then, two samplers are designed to enhance knowledge by sampling fuzzy samples with high uncertainty for obtaining user preferences and reliable negative samples for updating recommender to achieve efficient acquisition of user preferences and model updating, and thus provide a powerful solution for CRS to deal with E&E problem.
Adversarial Privacy-preserving Filter
While widely adopted in practical applications, face recognition has been critically discussed regarding the malicious use of face images and the potential privacy problems, e. g., deceiving payment system and causing personal sabotage.
