7 papers
Med-R: Enhancing Medical Retrieval-Augmented Reasoning of LLMs via Progressive Reinforcement Learning
Keer Lu, Zheng Liang, Youquan Li +8
In medical scenarios, effectively retrieving external knowledge and leveraging it for rigorous logical reasoning is of significant importance. Despite their potential, existing wor…
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…
The Hidden Cost of Readability: How Code Formatting Silently Consumes Your LLM Budget
Dangfeng Pan, Zhensu Sun, Cenyuan Zhang +2
Source code is usually formatted with elements like indentation and newlines to improve readability for human developers. However, these visual aids do not seem to be beneficial fo…
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…
Baichuan 2: Open Large-scale Language Models
Aiyuan Yang, Bin Xiao, Bingning Wang +52
Large language models (LLMs) have demonstrated remarkable performance on a variety of natural language tasks based on just a few examples of natural language instructions, reducing…
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…