3 citations · 4 across the 2 of their papers we have counts for
3 papers
cs.LG2024
HM3: Hierarchical Multi-Objective Model Merging for Pretrained Models
Yu Zhou, Xingyu Wu, Jibin Wu +2
Model merging is a technique that combines multiple large pretrained models into a single model with enhanced performance and broader task adaptability. It has gained popularity in…
cs.LG2024★ 1 cited
CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
Yu Zhou, Xingyu Wu, Jibin Wu +2
The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning, as causality reveals th…
cs.NE2024★ 3 cited
Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models
Beichen Huang, Xingyu Wu, Yu Zhou +4
Large language models (LLMs) have demonstrated exceptional performance not only in natural language processing tasks but also in a great variety of non-linguistic domains. In diver…