6 papers
AnyExperts: On-Demand Expert Allocation for Multimodal Language Models with Mixture of Expert
Yuting Gao, Wang Lan, Hengyuan Zhao +3
Multimodal Mixture-of-Experts (MoE) models offer a promising path toward scalable and efficient large vision-language systems. However, existing approaches rely on rigid routing st…
OSWorld-MCP: Benchmarking MCP Tool Invocation In Computer-Use Agents
Hongrui Jia, Jitong Liao, Xi Zhang +7
With advances in decision-making and reasoning capabilities, multimodal agents show strong potential in computer application scenarios. Past evaluations have mainly assessed GUI in…
VaccineRAG: Boosting Multimodal Large Language Models' Immunity to Harmful RAG Samples
Qixin Sun, Ziqin Wang, Hengyuan Zhao +6
Retrieval Augmented Generation enhances the response accuracy of Large Language Models (LLMs) by integrating retrieval and generation modules with external knowledge, demonstrating…
EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios
Bin Xu, Yu Bai, Huashan Sun +10
As large language models continue to advance, their application in educational contexts remains underexplored and under-optimized. In this paper, we address this gap by introducing…
LLaVA-CMoE: Towards Continual Mixture of Experts for Large Vision-Language Models
Hengyuan Zhao, Ziqin Wang, Qixin Sun +5
Mixture of Experts (MoE) architectures have recently advanced the scalability and adaptability of large language models (LLMs) for continual multimodal learning. However, efficient…
MIH-TCCT: Mitigating Inconsistent Hallucinations in LLMs via Event-Driven Text-Code Cyclic Training
Xinxin You, Xien Liu, Qixin Sun +7
Recent methodologies utilizing synthetic datasets have aimed to address inconsistent hallucinations in large language models (LLMs); however,these approaches are primarily tailored…