8 citations · 18 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis
Junying Chen, Chi Gui, Anningzhe Gao +4
The field of medical diagnosis has undergone a significant transformation with the advent of large language models (LLMs), yet the challenges of interpretability within these model…
LLMs for Doctors: Leveraging Medical LLMs to Assist Doctors, Not Replace Them
Wenya Xie, Qingying Xiao, Yu Zheng +7
The recent success of Large Language Models (LLMs) has had a significant impact on the healthcare field, providing patients with medical advice, diagnostic information, and more. H…
LLMs Could Autonomously Learn Without External Supervision
Ke Ji, Junying Chen, Anningzhe Gao +3
In the quest for super-human performance, Large Language Models (LLMs) have traditionally been tethered to human-annotated datasets and predefined training objectives-a process tha…
HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale
Junying Chen, Chi Gui, Ruyi Ouyang +10
The rapid development of multimodal large language models (MLLMs), such as GPT-4V, has led to significant advancements. However, these models still face challenges in medical multi…