8 citations · 11 across the 11 of their papers we have counts for
16 papers
Data Verification is the Future of Quantum Computing Copilots
Junhao Song, Ziqian Bi, Xinliang Chia +2
Quantum program generation demands a level of precision that may not be compatible with the statistical reasoning carried out in the inference of large language models (LLMs). Hall…
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…
Is GPT-OSS All You Need? Benchmarking Large Language Models for Financial Intelligence and the Surprising Efficiency Paradox
Ziqian Bi, Danyang Zhang, Junhao Song +1
The rapid adoption of large language models in financial services necessitates rigorous evaluation frameworks to assess their performance, efficiency, and practical applicability.…
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…
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…