6 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.AI2024★ 1 cited
Bench-CoE: a Framework for Collaboration of Experts from Benchmark
Yuanshuai Wang, Xingjian Zhang, Jinkun Zhao +5
Large Language Models (LLMs) are key technologies driving intelligent systems to handle multiple tasks. To meet the demands of various tasks, an increasing number of LLMs-driven ex…
cs.LG2022★ 1 cited
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks
Jiaqi Ma, Xingjian Zhang, Hezheng Fan +6
Establishing open and general benchmarks has been a critical driving force behind the success of modern machine learning techniques. As machine learning is being applied to broader…
cs.CL2022★ 6 cited
Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers
Weng Lam Tam, Xiao Liu, Kaixuan Ji +6
Prompt tuning attempts to update few task-specific parameters in pre-trained models. It has achieved comparable performance to fine-tuning of the full parameter set on both languag…