19 citations · 85 across the 29 of their papers we have counts for
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cs.LG2026
Davinci-Tool: Scaling Agentic Tool-Use Supervision with Verifiable Executable Environments
Xiaoyang Yuan, Ji Zhao, Xingyu Hu +7
Reliable agentic tool use requires training data that pair diverse multi-step workflows with executable evidence that the requested outcomes were achieved. Scaling either trajector…
cs.LG2024★ 8 cited
EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
Mengzhao Chen, Wenqi Shao, Peng Xu +4
Large language models (LLMs) are crucial in modern natural language processing and artificial intelligence. However, they face challenges in managing their significant memory requi…
cs.LG2024★ 3 cited
BESA: Pruning Large Language Models with Blockwise Parameter-Efficient Sparsity Allocation
Peng Xu, Wenqi Shao, Mengzhao Chen +6
Large language models (LLMs) have demonstrated outstanding performance in various tasks, such as text summarization, text question-answering, and etc. While their performance is im…