1 citations · 4 across the 12 of their papers we have counts for
7 papers · 1 filter
Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency
Yiran Huang, Lukas Thede, Massimiliano Mancini +2
While Large Vision Language Models (LVLMs) demonstrate impressive capabilities, their substantial computational and memory requirements pose deployment challenges on resource-const…
Dissecting Multimodal In-Context Learning: Modality Asymmetries and Circuit Dynamics in modern Transformers
Yiran Huang, Karsten Roth, Quentin Bouniot +2
Transformer-based multimodal large language models often exhibit in-context learning (ICL) abilities. Motivated by this phenomenon, we ask: how do transformers learn to associate i…
Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study
Yiran Huang, Lukas Thede, Massimiliano Mancini +2
While Multimodal Large Language Models (MLLMs) demonstrate impressive capabilities, their substantial computational and memory requirements pose significant barriers to practical d…
Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models
Jiaxin Zhang, Wendi Cui, Yiran Huang +2
Large language models (LLMs) are proficient in capturing factual knowledge across various domains. However, refining their capabilities on previously seen knowledge or integrating…
Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm
Lei Liu, Xiaoyan Yang, Fangzhou Li +10
Large language models (LLMs) are gaining increasing interests to improve clinical efficiency for medical diagnosis, owing to their unprecedented performance in modelling natural la…
RJUA-MedDQA: A Multimodal Benchmark for Medical Document Question Answering and Clinical Reasoning
Congyun Jin, Ming Zhang, Xiaowei Ma +13
Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis.…
RJUA-QA: A Comprehensive QA Dataset for Urology
Shiwei Lyu, Chenfei Chi, Hongbo Cai +14
We introduce RJUA-QA, a novel medical dataset for question answering (QA) and reasoning with clinical evidence, contributing to bridge the gap between general large language models…