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Found 7 papers, 2 papers with code
Leveraging Web-Crawled Data for High-Quality Fine-Tuning
Most large language models are fine-tuned using either expensive human-annotated data or GPT-4 generated data which cannot guarantee performance in certain domains.
SkinCAP: A Multi-modal Dermatology Dataset Annotated with Rich Medical Captions
To address this gap and provide a meticulously annotated dermatology dataset with comprehensive natural language descriptions, we introduce SkinCAP: a multi-modal dermatology dataset annotated with rich medical captions.
SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model
Skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases, impacting a considerable portion of the population.
Dialogue State Distillation Network with Inter-slot Contrastive Learning for Dialogue State Tracking
In this paper, we propose a Dialogue State Distillation Network (DSDN) to utilize relevant information of previous dialogue states and migrate the gap of utilization between training and testing.
A Transformer-Based User Satisfaction Prediction for Proactive Interaction Mechanism in DuerOS
Moreover, they are trained and evaluated on the benchmark datasets with adequate labels, which are expensive to obtain in a commercial dialogue system.
Inductive Matrix Completion Using Graph Autoencoder
However, without node content (i. e., side information) for training, the user (or item) specific representation can not be learned in the inductive setting, that is, a model trained on one group of users (or items) cannot adapt to new users (or items).
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Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing
Therefore, it is crucial to design a participant selection algorithm that applies to different MCS systems to achieve multiple goals.
