4 citations · 8 across the 12 of their papers we have counts for
13 papers
CodeMimicry: Exploiting Safety Generalization Lag in Large Language Models via Structured Code Completion
Zhen Liang, Hai Huang, Wentao Chen
Large language models have achieved remarkable capabilities across diverse domains, yet their safety alignment remains vulnerable to jailbreak attacks. In this work, we identify a…
EquaCode: A Multi-Strategy Jailbreak Approach for Large Language Models via Equation Solving and Code Completion
Zhen Liang, Hai Huang, Zhengkui Chen
Large language models (LLMs), such as ChatGPT, have achieved remarkable success across a wide range of fields. However, their trustworthiness remains a significant concern, as they…
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…
ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs
Weigang Lu, Ziyu Guan, Wei Zhao +5
GNN-to-MLP (G2M) methods have emerged as a promising approach to accelerate Graph Neural Networks (GNNs) by distilling their knowledge into simpler Multi-Layer Perceptrons (MLPs).…
Med-R: Enhancing Medical Retrieval-Augmented Reasoning of LLMs via Progressive Reinforcement Learning
Keer Lu, Zheng Liang, Youquan Li +7
In medical scenarios, effectively retrieving external knowledge and leveraging it for rigorous logical reasoning is of significant importance. Despite their potential, existing wor…
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…
DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Partitioning
Keer Lu, Xiaonan Nie, Zheng Liang +8
In recent years, Large Language Models (LLMs) have demonstrated significant improvements across a variety of tasks, one of which is the long-context capability. The key to improvin…
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…
FakeSound: Deepfake General Audio Detection
Zeyu Xie, Baihan Li, Xuenan Xu +3
With the advancement of audio generation, generative models can produce highly realistic audios. However, the proliferation of deepfake general audio can pose negative consequences…