most citedKnowledge Fusion of Large Language Models

8 citations · 25 across the 11 of their papers we have counts for

collaborators

12 papers

cs.CL2024

FuseChat: Knowledge Fusion of Chat Models

Fanqi Wan, Longguang Zhong, Ziyi Yang +2

While training large language models (LLMs) from scratch can indeed lead to models with distinct capabilities and strengths, it incurs substantial costs and may lead to redundancy…

cs.AI20244 cited

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…

cs.CL20248 cited

Knowledge Fusion of Large Language Models

Fanqi Wan, Xinting Huang, Deng Cai +3

While training large language models (LLMs) from scratch can generate models with distinct functionalities and strengths, it comes at significant costs and may result in redundant…

cs.CL20236 cited

PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection

Tao Yang, Tianyuan Shi, Fanqi Wan +4

Recent advances in large language models (LLMs), such as ChatGPT, have showcased remarkable zero-shot performance across various NLP tasks. However, the potential of LLMs in person…

cs.CL2023

Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration

Fanqi Wan, Xinting Huang, Tao Yang +3

Instruction-tuning can be substantially optimized through enhanced diversity, resulting in models capable of handling a broader spectrum of tasks. However, existing data employed f…

cs.CL20232 cited

Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems

Tianyuan Shi, Liangzhi Li, Zijian Lin +3

Efficient knowledge retrieval plays a pivotal role in ensuring the success of end-to-end task-oriented dialogue systems by facilitating the selection of relevant information necess…