127 citations · 133 across the 11 of their papers we have counts for
12 papers
Baichuan-M3: Modeling Clinical Inquiry for Reliable Medical Decision-Making
M3 Team, Chengfeng Dou, Fan Yang +15
We introduce Baichuan-M3, a medical-enhanced large language model engineered to shift the paradigm from passive question-answering to active, clinical-grade decision support. Addre…
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…
ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning
Mingyang Chen, Linzhuang Sun, Tianpeng Li +10
Large Language Models (LLMs) have shown remarkable capabilities in reasoning, exemplified by the success of OpenAI-o1 and DeepSeek-R1. However, integrating reasoning with external…
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…
MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification
Linzhuang Sun, Hao Liang, Jingxuan Wei +5
According to the Test-Time Scaling, the integration of External Slow-Thinking with the Verify mechanism has been demonstrated to enhance multi-round reasoning in large language mod…
Baichuan Alignment Technical Report
Mingan Lin, Fan Yang, Yanjun Shen +21
We introduce Baichuan Alignment, a detailed analysis of the alignment techniques employed in the Baichuan series of models. This represents the industry's first comprehensive accou…
Facilitating Multi-turn Function Calling for LLMs via Compositional Instruction Tuning
Mingyang Chen, Haoze Sun, Tianpeng Li +7
Large Language Models (LLMs) have exhibited significant potential in performing diverse tasks, including the ability to call functions or use external tools to enhance their perfor…
Baichuan-Omni Technical Report
Yadong Li, Haoze Sun, Mingan Lin +23
The salient multimodal capabilities and interactive experience of GPT-4o highlight its critical role in practical applications, yet it lacks a high-performing open-source counterpa…
BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline
Guosheng Dong, Da Pan, Yiding Sun +17
The general capabilities of Large Language Models (LLM) highly rely on the composition and selection on extensive pretraining datasets, treated as commercial secrets by several ins…
PAS: Data-Efficient Plug-and-Play Prompt Augmentation System
Miao Zheng, Hao Liang, Fan Yang +16
In recent years, the rise of Large Language Models (LLMs) has spurred a growing demand for plug-and-play AI systems. Among the various AI techniques, prompt engineering stands out…
MathScape: Benchmarking Multimodal Large Language Models in Real-World Mathematical Contexts
Hao Liang, Linzhuang Sun, Minxuan Zhou +7
With the rapid progress of Multimodal LLMs, evaluating their mathematical reasoning capabilities has become an increasingly important research direction. In particular, visual-text…
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…