5 papers
Benchmarking the Thinking Mode of Multimodal Large Language Models in Clinical Tasks
Jindong Hong, Tianjie Chen, Lingjie Luo +10
A recent advancement in Multimodal Large Language Models (MLLMs) research is the emergence of "reasoning MLLMs" that offer explicit control over their internal thinking processes (…
VERITAS: Leveraging Vision Priors and Expert Fusion to Improve Multimodal Data
Tingqiao Xu, Ziru Zeng, Jiayu Chen
The quality of supervised fine-tuning (SFT) data is crucial for the performance of large multimodal models (LMMs), yet current data enhancement methods often suffer from factual er…
Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel
Chuanyang Zheng, Jiankai Sun, Yihang Gao +13
Mixture-of-Experts (MoE) has become a cornerstone in recent state-of-the-art large language models (LLMs). Traditionally, MoE relies on as the router score funct…
Addressing accuracy and hallucination of LLMs in Alzheimer's disease research through knowledge graphs
Tingxuan Xu, Jiarui Feng, Justin Melendez +6
In the past two years, large language model (LLM)-based chatbots, such as ChatGPT, have revolutionized various domains by enabling diverse task completion and question-answering ca…
SeqPO-SiMT: Sequential Policy Optimization for Simultaneous Machine Translation
Ting Xu, Zhichao Huang, Jiankai Sun +2
We present Sequential Policy Optimization for Simultaneous Machine Translation (SeqPO-SiMT), a new policy optimization framework that defines the simultaneous machine translation (…