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Found 13 papers, 5 papers with code
TinyLLaVA: A Framework of Small-scale Large Multimodal Models
We present the TinyLLaVA framework that provides a unified perspective in designing and analyzing the small-scale Large Multimodal Models (LMMs).
Ranked #69 on
Visual Question Answering
on MM-Vet
Exploring semantic information in disease: Simple Data Augmentation Techniques for Chinese Disease Normalization
Nevertheless, the most significant obstacle to existing disease name normalization systems is the severe shortage of training data.
THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval
The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports (CTRs) and retrieve the corresponding evidence supporting the justification.
How can Deep Learning Retrieve the Write-Missing Additional Diagnosis from Chinese Electronic Medical Record For DRG
Under the circumstance that countries generally start to adopt DRG enrollment and payment, the problem of write-missing diagnosis is a common and serious problem.
Table-based Fact Verification with Self-adaptive Mixture of Experts
The table-based fact verification task has recently gained widespread attention and yet remains to be a very challenging problem.
THiFly\_Queens at SemEval-2021 Task 9: Two-stage Statement Verification with Adaptive Ensembling and Slot-based Operation
This paper describes our system for verifying statements with tables at SemEval-2021 Task 9.
GCN-BMP: Investigating Graph Representation Learning for DDI Prediction Task
To alleviate this problem, we investigate the utilization of the end-to-end graph representation learning for the DDI prediction task.
Label-guided Learning for Text Classification
In this study, we propose a label-guided learning framework LguidedLearn for text representation and classification.
Tensor Graph Convolutional Networks for Text Classification
A new framework TensorGCN (tensor graph convolutional networks), is presented for this task.
Language-independent Cross-lingual Contextual Representations
Contextual representation models like BERT have achieved state-of-the-art performance on a diverse range of NLP tasks.
Exploiting Sentence Embedding for Medical Question Answering
The learning framework consists of two main parts: 1) a sentence embedding producing module, and 2) a scoring module.
Medical Exam Question Answering with Large-scale Reading Comprehension
Reading and understanding text is one important component in computer aided diagnosis in clinical medicine, also being a major research problem in the field of NLP.
