9 citations · 12 across the 7 of their papers we have counts for
7 papers
Enhancing Faithfulness in Abstractive Summarization via Span-Level Fine-Tuning
Sicong Huang, Qianqi Yan, Shengze Wang +1
Abstractive summarization using large language models (LLMs) has become an essential tool for condensing information. However, despite their ability to generate fluent summaries, t…
Correct Reasoning Paths Visit Shared Decision Pivots
Dongkyu Cho, Amy B. Z. Zhang, Bilel Fehri +4
Chain-of-thought (CoT) reasoning exposes the intermediate thinking process of large language models (LLMs), yet verifying those traces at scale remains unsolved. In response, we in…
ReliableMath: Benchmark of Reliable Mathematical Reasoning on Large Language Models
Boyang Xue, Qi Zhu, Rui Wang +8
Although demonstrating remarkable performance on reasoning tasks, Large Language Models (LLMs) still tend to fabricate unreliable responses when confronted with problems that are u…
Foundation Model in Biomedicine
Xiangrui Liu, Yuanyuan Zhang, Qianyu Shang +14
Foundation models, first introduced in 2021, refer to large-scale pretrained models (e.g., large language models (LLMs) and vision-language models (VLMs)) that learn from extensive…
MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimization
Kangyu Zhu, Peng Xia, Yun Li +3
The advancement of Large Vision-Language Models (LVLMs) has propelled their application in the medical field. However, Medical LVLMs (Med-LVLMs) encounter factuality challenges due…
MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models
Peng Xia, Kangyu Zhu, Haoran Li +6
Artificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in disease diagnosis and treatment planning. Recent progress in Medical Large Vision…
CopilotCAD: Empowering Radiologists with Report Completion Models and Quantitative Evidence from Medical Image Foundation Models
Sheng Wang, Tianming Du, Katherine Fischer +5
Computer-aided diagnosis systems hold great promise to aid radiologists and clinicians in radiological clinical practice and enhance diagnostic accuracy and efficiency. However, th…