most citedMedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs

2 citations · 2 across the 7 of their papers we have counts for

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cs.CL2026

RATE: Reviewer Profiling and Annotation-free Training for Expertise Ranking in Peer Review Systems

Weicong Liu, Zixuan Yang, Yibo Zhao +1

Reviewer assignment is increasingly critical yet challenging in the LLM era, where rapid topic shifts render many pre-2023 benchmarks outdated and where proxy signals poorly reflec…

cs.CL2025

Think Globally, Group Locally: Evaluating LLMs Using Multi-Lingual Word Grouping Games

César Guerra-Solano, Zhuochun Li, Xiang Lorraine Li

Large language models (LLMs) can exhibit biases in reasoning capabilities due to linguistic modality, performing better on tasks in one language versus another, even with similar c…

cs.CL2025

MMBERT: Scaled Mixture-of-Experts Multimodal BERT for Robust Chinese Hate Speech Detection under Cloaking Perturbations

Qiyao Xue, Yuchen Dou, Ryan Shi +2

Hate speech detection on Chinese social networks presents distinct challenges, particularly due to the widespread use of cloaking techniques designed to evade conventional text-bas…

cs.CL20252 cited

MedReason: Eliciting Factual Medical Reasoning Steps in LLMs via Knowledge Graphs

Juncheng Wu, Wenlong Deng, Xingxuan Li +12

Medical tasks such as diagnosis and treatment planning require precise and complex reasoning, particularly in life-critical domains. Unlike mathematical reasoning, medical reasonin…

cs.CL2025

Similarity-Based Domain Adaptation with LLMs

Jie He, Wendi Zhou, Xiang Lorraine Li +1

Unsupervised domain adaptation leverages abundant labeled data from various source domains to generalize onto unlabeled target data. Prior research has primarily focused on learnin…

cs.CL2025

SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models

Xianfu Cheng, Wei Zhang, Shiwei Zhang +16

The increasing application of multi-modal large language models (MLLMs) across various sectors have spotlighted the essence of their output reliability and accuracy, particularly t…