15 citations · 29 across the 13 of their papers we have counts for
13 papers
Enhancing the Medical Context-Awareness Ability of LLMs via Multifaceted Self-Refinement Learning
Yuxuan Zhou, Yubin Wang, Bin Wang +4
Large language models (LLMs) have shown great promise in the medical domain, achieving strong performance on several benchmarks. However, they continue to underperform in real-worl…
Evaluating LLMs Across Multi-Cognitive Levels: From Medical Knowledge Mastery to Scenario-Based Problem Solving
Yuxuan Zhou, Xien Liu, Chenwei Yan +8
Large language models (LLMs) have demonstrated remarkable performance on various medical benchmarks, but their capabilities across different cognitive levels remain underexplored.…
LLM Sensitivity Evaluation Framework for Clinical Diagnosis
Chenwei Yan, Xiangling Fu, Yuxuan Xiong +4
Large language models (LLMs) have demonstrated impressive performance across various domains. However, for clinical diagnosis, higher expectations are required for LLM's reliabilit…
MIH-TCCT: Mitigating Inconsistent Hallucinations in LLMs via Event-Driven Text-Code Cyclic Training
Xinxin You, Xien Liu, Qixin Sun +7
Recent methodologies utilizing synthetic datasets have aimed to address inconsistent hallucinations in large language models (LLMs); however,these approaches are primarily tailored…
MKE-Coder: Multi-Axial Knowledge with Evidence Verification in ICD Coding for Chinese EMRs
Xinxin You, Xien Liu, Xue Yang +2
The task of automatically coding the International Classification of Diseases (ICD) in the medical field has been well-established and has received much attention. Automatic coding…
Data Augmentation Techniques for Chinese Disease Name Normalization
Wenqian Cui, Xiangling Fu, Shaohui Liu +4
Disease name normalization is an important task in the medical domain. It classifies disease names written in various formats into standardized names, serving as a fundamental comp…
Reliable and diverse evaluation of LLM medical knowledge mastery
Yuxuan Zhou, Xien Liu, Chen Ning +2
Mastering medical knowledge is crucial for medical-specific LLMs. However, despite the existence of medical benchmarks like MedQA, a unified framework that fully leverages existing…
MultifacetEval: Multifaceted Evaluation to Probe LLMs in Mastering Medical Knowledge
Yuxuan Zhou, Xien Liu, Chen Ning +1
Large language models (LLMs) have excelled across domains, also delivering notable performance on the medical evaluation benchmarks, such as MedQA. However, there still exists a si…
TinyLLaVA: A Framework of Small-scale Large Multimodal Models
Baichuan Zhou, Ying Hu, Xi Weng +5
We present the TinyLLaVA framework that provides a unified perspective in designing and analyzing the small-scale Large Multimodal Models (LMMs). We empirically study the effects o…
THiFLY Research at SemEval-2023 Task 7: A Multi-granularity System for CTR-based Textual Entailment and Evidence Retrieval
Yuxuan Zhou, Ziyu Jin, Meiwei Li +4
The NLI4CT task aims to entail hypotheses based on Clinical Trial Reports (CTRs) and retrieve the corresponding evidence supporting the justification. This task poses a significant…
Table-based Fact Verification with Self-adaptive Mixture of Experts
Yuxuan Zhou, Xien Liu, Kaiyin Zhou +1
The table-based fact verification task has recently gained widespread attention and yet remains to be a very challenging problem. It inherently requires informative reasoning over…
Label-guided Learning for Text Classification
Xien Liu, Song Wang, Xiao Zhang +3
Text classification is one of the most important and fundamental tasks in natural language processing. Performance of this task mainly dependents on text representation learning. C…
Tensor Graph Convolutional Networks for Text Classification
Xien Liu, Xinxin You, Xiao Zhang +2
Compared to sequential learning models, graph-based neural networks exhibit some excellent properties, such as ability capturing global information. In this paper, we investigate g…