33 citations · 162 across the 52 of their papers we have counts for
6 papers · 1 filter
Order-Level Attention Similarity Across Language Models: A Latent Commonality
Jinglin Liang, Jin Zhong, Shuangping Huang +5
In this paper, we explore an important yet previously neglected question: Do context aggregation patterns across Language Models (LMs) share commonalities? While some works have in…
PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation
Tao Fan, Guoqiang Ma, Yuanfeng Song +2
Compressing Large Language Models (LLMs) into task-specific Small Language Models (SLMs) encounters two significant challenges: safeguarding domain-specific knowledge privacy and m…
Federated Co-tuning Framework for Large and Small Language Models
Tao Fan, Yan Kang, Guoqiang Ma +4
By adapting Large Language Models (LLMs) to domain-specific tasks or enriching them with domain-specific knowledge, we can fully harness the capabilities of LLMs. Nonetheless, a ga…
FedCoT: Federated Chain-of-Thought Distillation for Large Language Models
Tao Fan, Weijing Chen, Yan Kang +5
Large Language Models (LLMs) have emerged as a transformative force in artificial intelligence, demonstrating exceptional proficiency across various tasks. However, their deploymen…
FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models
Tao Fan, Guoqiang Ma, Yan Kang +5
Recent research in federated large language models (LLMs) has primarily focused on enabling clients to fine-tune their locally deployed homogeneous LLMs collaboratively or on trans…
A Communication Theory Perspective on Prompting Engineering Methods for Large Language Models
Yuanfeng Song, Yuanqin He, Xuefang Zhao +5
The springing up of Large Language Models (LLMs) has shifted the community from single-task-orientated natural language processing (NLP) research to a holistic end-to-end multi-tas…