most citedDentalBench: Benchmarking and Advancing LLMs Capability for Bilingual Dentistry Understanding

1 citations · 1 across the 13 of their papers we have counts for

collaborators

16 papers

cs.CL2025

LongWeave: A Long-Form Generation Benchmark Bridging Real-World Relevance and Verifiability

Zikai Xiao, Fei Huang, Jianhong Tu +7

Generating long, informative, and factual outputs remains a major challenge for Large Language Models (LLMs). Existing benchmarks for long-form generation typically assess real-wor…

cs.CL20251 cited

DentalBench: Benchmarking and Advancing LLMs Capability for Bilingual Dentistry Understanding

Hengchuan Zhu, Yihuan Xu, Yichen Li +2

Recent advances in large language models (LLMs) and medical LLMs (Med-LLMs) have demonstrated strong performance on general medical benchmarks. However, their capabilities in speci…

cs.CL2025

Knowing or Guessing? Robust Medical Visual Question Answering via Joint Consistency and Contrastive Learning

Songtao Jiang, Yuxi Chen, Sibo Song +5

In high-stakes medical applications, consistent answering across diverse question phrasings is essential for reliable diagnosis. However, we reveal that current Medical Vision-Lang…

cs.CL2025

MedEthicsQA: A Comprehensive Question Answering Benchmark for Medical Ethics Evaluation of LLMs

Jianhui Wei, Zijie Meng, Zikai Xiao +5

While Medical Large Language Models (MedLLMs) have demonstrated remarkable potential in clinical tasks, their ethical safety remains insufficiently explored. This paper introduces…

cs.CV2025

SurgBench: A Unified Large-Scale Benchmark for Surgical Video Analysis

Jianhui Wei, Zikai Xiao, Danyu Sun +4

Surgical video understanding is pivotal for enabling automated intraoperative decision-making, skill assessment, and postoperative quality improvement. However, progress in develop…

cs.CL2025

Mitigating Posterior Salience Attenuation in Long-Context LLMs with Positional Contrastive Decoding

Zikai Xiao, Ziyang Wang, Wen Ma +5

While Large Language Models (LLMs) support long contexts, they struggle with performance degradation within the context window. Current solutions incur prohibitive training costs,…