9 citations · 51 across the 32 of their papers we have counts for
34 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…
AutoSurvey2: Empowering Researchers with Next Level Automated Literature Surveys
Siyi Wu, Chiaxin Liang, Ziqian Bi +7
The rapid growth of research literature, particularly in large language models (LLMs), has made producing comprehensive and current survey papers increasingly difficult. This paper…
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…
KokushiMD-10: Benchmark for Evaluating Large Language Models on Ten Japanese National Healthcare Licensing Examinations
Junyu Liu, Kaiqi Yan, Tianyang Wang +3
Recent advances in large language models (LLMs) have demonstrated notable performance in medical licensing exams. However, comprehensive evaluation of LLMs across various healthcar…
Predicting ICU In-Hospital Mortality Using Adaptive Transformer Layer Fusion
Han Wang, Ruoyun He, Guoguang Lao +16
Early identification of high-risk ICU patients is crucial for directing limited medical resources. We introduce ALFIA (Adaptive Layer Fusion with Intelligent Attention), a modular,…
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models
Junjie Yang, Junhao Song, Xudong Han +9
Knowledge distillation (KD) is a technique for transferring knowledge from complex teacher models to simpler student models, significantly enhancing model efficiency and accuracy.…
Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement
Chiung-Yi Tseng, Junhao Song, Ziqian Bi +4
In the era of data-driven intelligence, the paradox of data abundance and annotation scarcity has emerged as a critical bottleneck in the advancement of machine learning. This pape…
Advanced Deep Learning Methods for Protein Structure Prediction and Design
Yichao Zhang, Ningyuan Deng, Xinyuan Song +25
After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to prote…
Generative Adversarial Networks Bridging Art and Machine Intelligence
Junhao Song, Yichao Zhang, Ziqian Bi +25
Generative Adversarial Networks (GAN) have greatly influenced the development of computer vision and artificial intelligence in the past decade and also connected art and machine i…
From In Silico to In Vitro: A Comprehensive Guide to Validating Bioinformatics Findings
Tianyang Wang, Silin Chen, Yunze Wang +18
Translating computational predictions into experimentally validated biological knowledge remains one of the central challenges in modern bioinformatics. This review critically exam…
From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
Tianyang Wang, Yunze Wang, Jun Zhou +16
Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financia…
Deep Learning Model Security: Threats and Defenses
Tianyang Wang, Ziqian Bi, Yichao Zhang +24
Deep learning has transformed AI applications but faces critical security challenges, including adversarial attacks, data poisoning, model theft, and privacy leakage. This survey e…
From Noise to Nuance: Advances in Deep Generative Image Models
Benji Peng, Chia Xin Liang, Ziqian Bi +6
Deep learning-based image generation has undergone a paradigm shift since 2021, marked by fundamental architectural breakthroughs and computational innovations. Through reviewing a…
A Comprehensive Guide to Explainable AI: From Classical Models to LLMs
Weiche Hsieh, Ziqian Bi, Chuanqi Jiang +24
Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making proce…
Deep Learning, Machine Learning, Advancing Big Data Analytics and Management
Weiche Hsieh, Ziqian Bi, Keyu Chen +23
Advancements in artificial intelligence, machine learning, and deep learning have catalyzed the transformation of big data analytics and management into pivotal domains for researc…
A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks
Chia Xin Liang, Pu Tian, Caitlyn Heqi Yin +7
This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact…
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models
Charles Zhang, Benji Peng, Xintian Sun +14
Word embeddings and language models have transformed natural language processing (NLP) by facilitating the representation of linguistic elements in continuous vector spaces. This r…
Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application
Keyu Chen, Cheng Fei, Ziqian Bi +23
With a focus on natural language processing (NLP) and the role of large language models (LLMs), we explore the intersection of machine learning, deep learning, and artificial intel…
Deep Learning, Machine Learning -- Digital Signal and Image Processing: From Theory to Application
Weiche Hsieh, Ziqian Bi, Junyu Liu +16
Digital Signal Processing (DSP) and Digital Image Processing (DIP) with Machine Learning (ML) and Deep Learning (DL) are popular research areas in Computer Vision and related field…
Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice
Silin Chen, Ziqian Bi, Junyu Liu +15
This book provides a comprehensive introduction to the foundational concepts of machine learning (ML) and deep learning (DL). It bridges the gap between theoretical mathematics and…