activity
20222026
most citedTable-based Fact Verification with Self-adaptive Mixture of Experts

1 citations · 1 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2026

Is GraphRAG Needed? From Basic RAG to Graph-/Agentic Solutions with Context Optimization

Long Chen, Ryan Razkenari, Yuxuan Zhou +5

As advanced RAG variants like GraphRAG and Agentic RAG emerge, one leading question is when and how to use them. Here, we introduce a framework for different RAG scenarios evaluati…

cs.CR2026

The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems

Yihao Zhang, Kai Wang, Jiangrong Wu +7

Large Language Models (LLMs) face prominent security risks from jailbreaking, a practice that manipulates models to bypass built-in security constraints and generate unethical or u…

cs.AI2025

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…

cs.CL2025

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.…

cs.CL2024

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…

cs.AI20221 cited

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…