7 citations · 18 across the 15 of their papers we have counts for
16 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…
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…
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…
Prior-free Balanced Replay: Uncertainty-guided Reservoir Sampling for Long-Tailed Continual Learning
Lei Liu, Li Liu, Yawen Cui
Even in the era of large models, one of the well-known issues in continual learning (CL) is catastrophic forgetting, which is significantly challenging when the continual data stre…
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…
Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm
Lei Liu, Xiaoyan Yang, Fangzhou Li +10
Large language models (LLMs) are gaining increasing interests to improve clinical efficiency for medical diagnosis, owing to their unprecedented performance in modelling natural la…
Computation and Parameter Efficient Multi-Modal Fusion Transformer for Cued Speech Recognition
Lei Liu, Li Liu, Haizhou Li
Cued Speech (CS) is a pure visual coding method used by hearing-impaired people that combines lip reading with several specific hand shapes to make the spoken language visible. Aut…
RJUA-QA: A Comprehensive QA Dataset for Urology
Shiwei Lyu, Chenfei Chi, Hongbo Cai +14
We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models…
From Beginner to Expert: Modeling Medical Knowledge into General LLMs
Qiang Li, Xiaoyan Yang, Haowen Wang +14
Recently, large language model (LLM) based artificial intelligence (AI) systems have demonstrated remarkable capabilities in natural language understanding and generation. However,…
Think-in-Memory: Recalling and Post-thinking Enable LLMs with Long-Term Memory
Lei Liu, Xiaoyan Yang, Yue Shen +4
Memory-augmented Large Language Models (LLMs) have demonstrated remarkable performance in long-term human-machine interactions, which basically relies on iterative recalling and re…
Cuing Without Sharing: A Federated Cued Speech Recognition Framework via Mutual Knowledge Distillation
Yuxuan Zhang, Lei Liu, Li Liu
Cued Speech (CS) is a visual coding tool to encode spoken languages at the phonetic level, which combines lip-reading and hand gestures to effectively assist communication among pe…
Spatio-Temporal Structure Consistency for Semi-supervised Medical Image Classification
Wentao Lei, Lei Liu, Li Liu
Intelligent medical diagnosis has shown remarkable progress based on the large-scale datasets with precise annotations. However, fewer labeled images are available due to significa…
Cross-Modal Mutual Learning for Cued Speech Recognition
Lei Liu, Li Liu
Automatic Cued Speech Recognition (ACSR) provides an intelligent human-machine interface for visual communications, where the Cued Speech (CS) system utilizes lip movements and han…
Semi-Supervised Active Learning for COVID-19 Lung Ultrasound Multi-symptom Classification
Lei Liu, Wentao Lei, Yongfang Luo +3
Ultrasound (US) is a non-invasive yet effective medical diagnostic imaging technique for the COVID-19 global pandemic. However, due to complex feature behaviors and expensive annot…
Person Re-Identification via Active Hard Sample Mining
Xin Xu, Lei Liu, Weifeng Liu +2
Annotating a large-scale image dataset is very tedious, yet necessary for training person re-identification models. To alleviate such a problem, we present an active hard sample mi…