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20242026
most citedDentalBench: Benchmarking and Advancing LLMs Capability for Bilingual Dentistry Understanding

1 citations · 3 across the 24 of their papers we have counts for

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cs.CL2026

MedUAG: Unified Understanding and Generation for Medical Multimodal Models

Zijie Meng, Yuncheng Zhang, Hualiang Wang +8

Recent Multimodal Large Language Models (MLLMs) are rapidly evolving into unified understanding and generation (UAG) frameworks. However, extending these unified paradigms to the m…

cs.CL2025★ 1 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

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.CL2025

OmniV-Med: Scaling Medical Vision-Language Model for Universal Visual Understanding

Songtao Jiang, Yuan Wang, Sibo Song +6

The practical deployment of medical vision-language models (Med-VLMs) necessitates seamless integration of textual data with diverse visual modalities, including 2D/3D images and v…

cs.CL2024

EC-Guide: A Comprehensive E-Commerce Guide for Instruction Tuning and Quantization

Zhaopeng Feng, Zijie Meng, Zuozhu Liu

Large language models (LLMs) have attracted considerable attention in various fields for their cost-effective solutions to diverse challenges, especially with advancements in instr…

cs.CL2024★ 1 cited

Ladder: A Model-Agnostic Framework Boosting LLM-based Machine Translation to the Next Level

Zhaopeng Feng, Ruizhe Chen, Yan Zhang +2

General-purpose Large Language Models (LLMs) like GPT-4 have achieved remarkable advancements in machine translation (MT) by leveraging extensive web content. On the other hand, tr…

cs.CL2024

DCR: Divide-and-Conquer Reasoning for Multi-choice Question Answering with LLMs

Zijie Meng, Yan Zhang, Zhaopeng Feng +1

Large language models (LLMs) have shown impressive performance in reasoning benchmarks with the emergence of Chain-of-Thought (CoT), particularly in multi-choice question (MCQ). Ho…