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Found 5 papers, 1 papers with code
SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding Altering
In particular, local editing methods, which directly update model parameters, are more suitable for updating a small amount of knowledge.
How to Bridge the Gap between Modalities: A Comprehensive Survey on Multimodal Large Language Model
The study surveys existing modal alignment methods in MLLMs into four groups: (1) Multimodal Converters that change data into something LLMs can understand; (2) Multimodal Perceivers to improve how LLMs perceive different types of data; (3) Tools Assistance for changing data into one common format, usually text; and (4) Data-Driven methods that teach LLMs to understand specific types of data in a dataset.
Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation
Conventional graph neural network based methods usually deal with each domain separately, or train a shared model to serve all domains.
Posterior Probability Matters: Doubly-Adaptive Calibration for Neural Predictions in Online Advertising
Calibration techniques aims to post-process model predictions to posterior probabilities.
UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation
Then a student model is trained on both clicked and unclicked ads with knowledge distillation, performing uncertainty modeling to alleviate the inherent noise in pseudo-labels.
