Showing cs.CLShow all
3 papers · 1 filter
cs.CL2025
Are We on the Right Way for Assessing Document Retrieval-Augmented Generation?
Wenxuan Shen, Mingjia Wang, Yaochen Wang +4
Retrieval-Augmented Generation (RAG) systems using Multimodal Large Language Models (MLLMs) show great promise for complex document understanding, yet their development is critical…
cs.CL2025
Judge Anything: MLLM as a Judge Across Any Modality
Shu Pu, Yaochen Wang, Dongping Chen +10
Evaluating generative foundation models on open-ended multimodal understanding (MMU) and generation (MMG) tasks across diverse modalities (e.g., images, audio, video) poses signifi…
cs.CL2024
What can LLM tell us about cities?
Zhuoheng Li, Yaochen Wang, Zhixue Song +4
This study explores the capabilities of large language models (LLMs) in providing knowledge about cities and regions on a global scale. We employ two methods: directly querying the…