64 citations · 69 across the 5 of their papers we have counts for
5 papers
Dual Debiasing for Noisy In-Context Learning for Text Generation
Siqi Liang, Sumyeong Ahn, Paramveer S. Dhillon +1
In context learning (ICL) relies heavily on high quality demonstrations drawn from large annotated corpora. Existing approaches detect noisy annotations by ranking local perplexiti…
Distributed In-Context Learning under Non-IID Among Clients
Siqi Liang, Sumyeong Ahn, Jiayu Zhou
Advancements in large language models (LLMs) have shown their effectiveness in multiple complicated natural language reasoning tasks. A key challenge remains in adapting these mode…
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…
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning
Dun Zeng, Shiyu Liu, Siqi Liang +4
Federated learning enables isolated clients to train a shared model collaboratively by aggregating the locally-computed gradient updates. However, privacy information could be leak…
FedLab: A Flexible Federated Learning Framework
Dun Zeng, Siqi Liang, Xiangjing Hu +2
Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations.…