5 papers
Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training
Lei Liu, Hao Zhu, Yue Shen +4
Continual Pre-training (CPT) serves as a fundamental approach for adapting foundation models to domain-specific applications. Scaling laws for pre-training define a power-law relat…
AnomalyControl: Learning Cross-modal Semantic Features for Controllable Anomaly Synthesis
Shidan He, Lei Liu, Xiujun Shu +3
Anomaly synthesis is a crucial approach to augment abnormal data for advancing anomaly inspection. Based on the knowledge from the large-scale pre-training, existing text-to-image…
PRGB Benchmark: A Robust Placeholder-Assisted Algorithm for Benchmarking Retrieval-Augmented Generation
Zhehao Tan, Yihan Jiao, Dan Yang +7
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge, where the LLM's ability to generate responses based on the combination…
Reliable Imputed-Sample Assisted Vertical Federated Learning
Yaopei Zeng, Lei Liu, Shaoguo Liu +3
Vertical Federated Learning (VFL) is a well-known FL variant that enables multiple parties to collaboratively train a model without sharing their raw data. Existing VFL approaches…
A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions
Lei Liu, Xiaoyan Yang, Junchi Lei +6
With the advent of Large Language Models (LLMs), medical artificial intelligence (AI) has experienced substantial technological progress and paradigm shifts, highlighting the poten…