Search Results for author:
Found 9 papers, 6 papers with code
A Survey of Large Language Models in Medicine: Progress, Application, and Challenge
Therefore, this review aims to provide a detailed overview of the development and deployment of LLMs in medicine, including the challenges and opportunities they face.
Exploring Recommendation Capabilities of GPT-4V(ision): A Preliminary Case Study
Large Multimodal Models (LMMs) have demonstrated impressive performance across various vision and language tasks, yet their potential applications in recommendation tasks with visual assistance remain unexplored.
Qilin-Med-VL: Towards Chinese Large Vision-Language Model for General Healthcare
This method enhances the model's ability to generate medical captions and answer complex medical queries.
Qilin-Med: Multi-stage Knowledge Injection Advanced Medical Large Language Model
In the CPT and SFT phases, Qilin-Med achieved 38. 4% and 40. 0% accuracy on the CMExam test set, respectively.
LLMRec: Benchmarking Large Language Models on Recommendation Task
The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset
To the best of our knowledge, CMExam is the first Chinese medical exam dataset to provide comprehensive medical annotations.
Is ChatGPT a Good Recommender? A Preliminary Study
We conduct human evaluations on two explainability-oriented tasks to more accurately evaluate the quality of contents generated by different models.
Weighing Features of Lung and Heart Regions for Thoracic Disease Classification
By zeroing features of non-lung and heart regions in attention maps, we can effectively exploit their disease-specific cues in lung and heart regions.
Spatial-Scale Aligned Network for Fine-Grained Recognition
Existing approaches for fine-grained visual recognition focus on learning marginal region-based representations while neglecting the spatial and scale misalignments, leading to inferior performance.
