15 citations · 45 across the 14 of their papers we have counts for
14 papers
Adaptive Draft-Verification for Efficient Large Language Model Decoding
Xukun Liu, Bowen Lei, Ruqi Zhang +1
Large language model (LLM) decoding involves generating a sequence of tokens based on a given context, where each token is predicted one at a time using the model's learned probabi…
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World
Bowen Lei, Dongkuan Xu, Ruqi Zhang +1
Sparse training has emerged as a promising method for resource-efficient deep neural networks (DNNs) in real-world applications. However, the reliability of sparse models remains a…
On the Essence and Prospect: An Investigation of Alignment Approaches for Big Models
Xinpeng Wang, Shitong Duan, Xiaoyuan Yi +7
Big models have achieved revolutionary breakthroughs in the field of AI, but they might also pose potential concerns. Addressing such concerns, alignment technologies were introduc…
ToolNet: Connecting Large Language Models with Massive Tools via Tool Graph
Xukun Liu, Zhiyuan Peng, Xiaoyuan Yi +4
While achieving remarkable progress in a broad range of tasks, large language models (LLMs) remain significantly limited in properly using massive external tools. Existing in-conte…
Gentopia: A Collaborative Platform for Tool-Augmented LLMs
Binfeng Xu, Xukun Liu, Hua Shen +7
Augmented Language Models (ALMs) empower large language models with the ability to use tools, transforming them into intelligent agents for real-world interactions. However, most e…
Towards Personalized Federated Learning via Heterogeneous Model Reassembly
Jiaqi Wang, Xingyi Yang, Suhan Cui +4
This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures.…