4 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.AI2024★ 3 cited
UniDM: A Unified Framework for Data Manipulation with Large Language Models
Yichen Qian, Yongyi He, Rong Zhu +8
Designing effective data manipulation methods is a long standing problem in data lakes. Traditional methods, which rely on rules or machine learning models, require extensive human…
cs.CR2024★ 1 cited
PPFedIT: Towards Privacy-Preserving Federated Instruction Tuning with Few-shot Local Examples
Zhuo Zhang, Jingyuan Zhang, Jintao Huang +7
Instruction tuning aligns large language models (LLMs) with human intentions but requires diverse, high-quality data that are difficult to collect in privacy-sensitive domains. Fed…
cs.LG2023★ 4 cited
FedNoisy: Federated Noisy Label Learning Benchmark
Siqi Liang, Jintao Huang, Junyuan Hong +3
Federated learning has gained popularity for distributed learning without aggregating sensitive data from clients. But meanwhile, the distributed and isolated nature of data isolat…