16 citations · 16 across the 7 of their papers we have counts for
8 papers
EMIR: Evolution-Aware Memory with Intent-Guided Multi-Round Retrieval
Jinlan Liu, Hongliang Sun, Yong Wang +4
Long-term memory enables large language model (LLM) agents to leverage historical interactions for future tasks. However, existing memory systems struggle to utilize continuously e…
MADE: Belief-Driven Dual-Agent Coordination for Autonomous Model Deployment
Yicheng Liu, Bolin Zhang, Weiran Liu +4
LLM-based agents now have strong general capabilities. However, they still struggle with domain-specific tasks, motivating the integration of external tools to broaden their capabi…
Dual-Path LLM Reasoning for Multimodal Few-Shot Knowledge Graph Completion
Jinlan Liu, Zhiying Tu, Yongchao Xing +5
Knowledge graph completion (KGC) aims to infer missing facts in knowledge graphs (KGs), thereby improving their completeness and supporting downstream intelligent applications. How…
An Effective Router for Vision-Language Model Selection
Can Wang, Shengwei Wang, Bolin Zhang +2
Vision-language models (VLMs) with varying performance and resource requirements are widely deployed, making it difficult for users to select the most appropriate one among numerou…
Evidence-Based Intelligent Diagnostic and Therapeutic Visualization System with Large Language Models: Multi-Turn Interaction and Multimodal Treatment Plan Generation
Yunhan Wang, Yuda Wang, Zhiying Tu +5
Aim: Existing AI-assisted traditional Chinese medicine diagnostic tools suffer from opaque reasoning processes, passive interaction, and limited treatment plan presentation. This s…
Plug-and-Play Performance Estimation for LLM Services without Relying on Labeled Data
Can Wang, Dianbo Sui, Hongliang Sun +3
Large Language Model (LLM) services exhibit impressive capability on unlearned tasks leveraging only a few examples by in-context learning (ICL). However, the success of ICL varies…
A Survey on Data Selection for LLM Instruction Tuning
Bolin Zhang, Jiahao Wang, Qianlong Du +3
Instruction tuning is a vital step of training large language models (LLMs), so how to enhance the effect of instruction tuning has received increased attention. Existing works ind…
A Framework for Effective Invocation Methods of Various LLM Services
Can Wang, Dianbo Sui, Bolin Zhang +4
Large Language Models (LLMs) have shown impressive abilities in solving various natural language processing tasks and are now widely offered as services. LLM services enable users…