1 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.CL2024★ 1 cited
Mitigating Training Imbalance in LLM Fine-Tuning via Selective Parameter Merging
Yiming Ju, Ziyi Ni, Xingrun Xing +4
Supervised fine-tuning (SFT) is crucial for adapting Large Language Models (LLMs) to specific tasks. In this work, we demonstrate that the order of training data can lead to signif…
cs.CV2024★ 1 cited
RCooper: A Real-world Large-scale Dataset for Roadside Cooperative Perception
Ruiyang Hao, Siqi Fan, Yingru Dai +7
The value of roadside perception, which could extend the boundaries of autonomous driving and traffic management, has gradually become more prominent and acknowledged in recent yea…