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Found 6 papers, 3 papers with code
Protecting Your LLMs with Information Bottleneck
The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content.
Explaining Time Series via Contrastive and Locally Sparse Perturbations
Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns.
RCAgent: Cloud Root Cause Analysis by Autonomous Agents with Tool-Augmented Large Language Models
Large language model (LLM) applications in cloud root cause analysis (RCA) have been actively explored recently.
Multi-factor Sequential Re-ranking with Perception-Aware Diversification
Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications.
GPatch: Patching Graph Neural Networks for Cold-Start Recommendations
State-of-the-art solutions rely on training hybrid models for both cold-start and existing users/items, based on the auxiliary information.
Generative Adversarial Framework for Cold-Start Item Recommendation
Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions.
