2 citations · 4 across the 6 of their papers we have counts for
12 papers
Baichuan-M2: Scaling Medical Capability with Large Verifier System
M2 Team, Chengfeng Dou, Chong Liu +31
As large language models (LLMs) advance in conversational and reasoning capabilities, their practical application in healthcare has become a critical research focus. However, there…
Baichuan-M1: Pushing the Medical Capability of Large Language Models
Bingning Wang, Haizhou Zhao, Huozhi Zhou +39
The current generation of large language models (LLMs) is typically designed for broad, general-purpose applications, while domain-specific LLMs, especially in vertical fields like…
Baichuan-Audio: A Unified Framework for End-to-End Speech Interaction
Tianpeng Li, Jun Liu, Tao Zhang +11
We introduce Baichuan-Audio, an end-to-end audio large language model that seamlessly integrates audio understanding and generation. It features a text-guided aligned speech genera…
Baichuan-Omni-1.5 Technical Report
Yadong Li, Jun Liu, Tao Zhang +89
We introduce Baichuan-Omni-1.5, an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities. To achieve f…
Med-R: Crafting Trustworthy LLM Physicians via Retrieval and Reasoning of Evidence-Based Medicine
Keer Lu, Zheng Liang, Da Pan +6
Large Language Models (LLMs) have exhibited remarkable capabilities in clinical scenarios. Despite their potential, existing works face challenges when applying LLMs to medical set…
VersaTune: An Efficient Data Composition Framework for Training Multi-Capability LLMs
Keer Lu, Keshi Zhao, Zhuoran Zhang +8
As demonstrated by the proprietary Large Language Models (LLMs) such as GPT and Claude series, LLMs have the potential to achieve remarkable proficiency across a wide range of doma…