4 citations · 10 across the 10 of their papers we have counts for
5 papers · 1 filter
ProFuser: Progressive Fusion of Large Language Models
Tianyuan Shi, Fanqi Wan, Canbin Huang +6
While fusing the capacities and advantages of various large language models offers a pathway to construct more powerful and versatile models, a fundamental challenge is to properly…
Cool-Fusion: Fuse Large Language Models without Training
Cong Liu, Xiaojun Quan, Yan Pan +3
We focus on the problem of fusing two or more heterogeneous large language models (LLMs) to leverage their complementary strengths. One of the challenges of model fusion is high co…
SocialBench: Sociality Evaluation of Role-Playing Conversational Agents
Hongzhan Chen, Hehong Chen, Ming Yan +8
Large language models (LLMs) have advanced the development of various AI conversational agents, including role-playing conversational agents that mimic diverse characters and human…
Small LLMs Are Weak Tool Learners: A Multi-LLM Agent
Weizhou Shen, Chenliang Li, Hongzhan Chen +5
Large Language Model (LLM) agents significantly extend the capabilities of standalone LLMs, empowering them to interact with external tools (e.g., APIs, functions) and complete var…
Knowledge Distillation of Black-Box Large Language Models
Hongzhan Chen, Ruijun Chen, Yuqi Yi +4
Given the exceptional performance of proprietary large language models (LLMs) like GPT-4, recent research has increasingly focused on boosting the capabilities of smaller models th…