6 citations · 11 across the 10 of their papers we have counts for
11 papers · 1 filter
Enabling Doctor-Centric Medical AI with LLMs through Workflow-Aligned Tasks and Benchmarks
Wenya Xie, Qingying Xiao, Yu Zheng +8
The rise of large language models (LLMs) has transformed healthcare by offering clinical guidance, yet their direct deployment to patients poses safety risks due to limited domain…
ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine
Junying Chen, Zhenyang Cai, Zhiheng Liu +10
Despite the success of large language models (LLMs) in various domains, their potential in Traditional Chinese Medicine (TCM) remains largely underexplored due to two critical barr…
The First Few Tokens Are All You Need: An Efficient and Effective Unsupervised Prefix Fine-Tuning Method for Reasoning Models
Ke Ji, Jiahao Xu, Tian Liang +10
Improving the reasoning capabilities of large language models (LLMs) typically requires supervised fine-tuning with labeled data or computationally expensive sampling. We introduce…
RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions
Wanlong Liu, Junying Chen, Ke Ji +3
Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models (LLMs) by incorporating external knowledge. However, current RAG methods face…
HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs
Junying Chen, Zhenyang Cai, Ke Ji +5
The breakthrough of OpenAI o1 highlights the potential of enhancing reasoning to improve LLM. Yet, most research in reasoning has focused on mathematical tasks, leaving domains lik…
Alignment at Pre-training! Towards Native Alignment for Arabic LLMs
Juhao Liang, Zhenyang Cai, Jianqing Zhu +9
The alignment of large language models (LLMs) is critical for developing effective and safe language models. Traditional approaches focus on aligning models during the instruction…