6 papers
AsynDBT: Asynchronous Distributed Bilevel Tuning for efficient In-Context Learning with Large Language Models
Hui Ma, Shaoyu Dou, Ya Liu +3
With the rapid development of large language models (LLMs), an increasing number of applications leverage cloud-based LLM APIs to reduce usage costs. However, since cloud-based mod…
Sim-MSTNet: sim2real based Multi-task SpatioTemporal Network Traffic Forecasting
Hui Ma, Qingzhong Li, Jin Wang +4
Network traffic forecasting plays a crucial role in intelligent network operations, but existing techniques often perform poorly when faced with limited data. Additionally, multi-t…
Learning Longitudinal Health Representations from EHR and Wearable Data
Yuanyun Zhang, Han Zhou, Li Feng +2
Foundation models trained on electronic health records show strong performance on many clinical prediction tasks but are limited by sparse and irregular documentation. Wearable dev…
Statement-Tuning Enables Efficient Cross-lingual Generalization in Encoder-only Models
Ahmed Elshabrawy, Thanh-Nhi Nguyen, Yeeun Kang +8
Large Language Models (LLMs) excel in zero-shot and few-shot tasks, but achieving similar performance with encoder-only models like BERT and RoBERTa has been challenging due to the…
PediaBench: A Comprehensive Chinese Pediatric Dataset for Benchmarking Large Language Models
Qian Zhang, Panfeng Chen, Jiali Li +6
The emergence of Large Language Models (LLMs) in the medical domain has stressed a compelling need for standard datasets to evaluate their question-answering (QA) performance. Alth…
CM-DQN: A Value-Based Deep Reinforcement Learning Model to Simulate Confirmation Bias
Jiacheng Shen, Lihan Feng
In human decision-making tasks, individuals learn through trials and prediction errors. When individuals learn the task, some are more influenced by good outcomes, while others wei…