1 citations · 2 across the 5 of their papers we have counts for
5 papers
Semi-Supervised Synthetic Data Generation with Fine-Grained Relevance Control for Short Video Search Relevance Modeling
Haoran Li, Zhiming Su, Junyan Yao +6
Synthetic data is widely adopted in embedding models to ensure diversity in training data distributions across dimensions such as difficulty, length, and language. However, existin…
Multi-Modal Prompt Learning on Blind Image Quality Assessment
Wensheng Pan, Timin Gao, Yan Zhang +10
Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Currently, leveraging seman…
Sinkhorn Distance Minimization for Knowledge Distillation
Xiao Cui, Yulei Qin, Yuting Gao +7
Knowledge distillation (KD) has been widely adopted to compress large language models (LLMs). Existing KD methods investigate various divergence measures including the Kullback-Lei…
MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples
Tao Chen, Enwei Zhang, Yuting Gao +5
Although In-Context Learning (ICL) brings remarkable performance gains to Large Language Models (LLMs), the improvements remain lower than fine-tuning on downstream tasks. This pap…
Towards Robust Text Retrieval with Progressive Learning
Tong Wu, Yulei Qin, Enwei Zhang +4
Retrieval augmentation has become an effective solution to empower large language models (LLMs) with external and verified knowledge sources from the database, which overcomes the…