1 citations · 1 across the 8 of their papers we have counts for
8 papers
47B Mixture-of-Experts Beats 671B Dense Models on Chinese Medical Examinations
Chiung-Yi Tseng, Danyang Zhang, Tianyang Wang +8
The rapid advancement of large language models(LLMs) has prompted significant interest in their potential applications in medical domains. This paper presents a comprehensive bench…
CoT-X: An Adaptive Framework for Cross-Model Chain-of-Thought Transfer and Optimization
Ziqian Bi, Yinzhi Wang, Tianyang Wang +6
Long Chain-of-Thought (CoT) traces can improve reasoning accuracy, but repeatedly generating them is costly for smaller or latency-constrained language models. This paper studies a…
Towards Alignment-Centric Paradigm: A Survey of Instruction Tuning in Large Language Models
Xudong Han, Junjie Yang, Tianyang Wang +4
Instruction tuning is a pivotal technique for aligning large language models (LLMs) with human intentions, safety constraints, and domain-specific requirements. This survey provide…
Is GPT-OSS Good? A Comprehensive Evaluation of OpenAI's Latest Open Source Models
Ziqian Bi, Keyu Chen, Chiung-Yi Tseng +9
In August 2025, OpenAI released GPT-OSS models, its first open weight large language models since GPT-2 in 2019, comprising two mixture of experts architectures with 120B and 20B p…
Exploring Efficiency Frontiers of Thinking Budget in Medical Reasoning: Scaling Laws between Computational Resources and Reasoning Quality
Ziqian Bi, Lu Chen, Junhao Song +15
This study presents the first comprehensive evaluation of thinking budget mechanisms in medical reasoning tasks, revealing fundamental scaling laws between computational resources…
Mixture of Experts in Large Language Models
Danyang Zhang, Junhao Song, Ziqian Bi +5
This paper presents a comprehensive review of the Mixture-of-Experts (MoE) architecture in large language models, highlighting its ability to significantly enhance model performanc…
Multimodal Representation Learning and Fusion
Qihang Jin, Enze Ge, Yuhang Xie +8
Multi-modal learning is a fast growing area in artificial intelligence. It tries to help machines understand complex things by combining information from different sources, like im…
Achieving Trustworthy Real-Time Decision Support Systems with Low-Latency Interpretable AI Models
Zechun Deng, Ziwei Liu, Ziqian Bi +5
This paper investigates real-time decision support systems that leverage low-latency AI models, bringing together recent progress in holistic AI-driven decision tools, integration…