Výsledky vyhledávání - "Huang, Weizhe"

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Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models

Autor: Liu, Tongxuan, Xu, Wenjiang, Huang, Weizhe, Wang, Xingyu, Wang, Jiaxing, Yang, Hailong, Li, Jing

Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks but their performance in complex logical reasoning tasks remains unsatisfactory. Although some prompting methods, such as Chain-of-Thought, can improve the re

Externí odkaz: http://arxiv.org/abs/2409.17539

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GroupDebate: Enhancing the Efficiency of Multi-Agent Debate Using Group Discussion

Autor: Liu, Tongxuan, Wang, Xingyu, Huang, Weizhe, Xu, Wenjiang, Zeng, Yuting, Jiang, Lei, Yang, Hailong, Li, Jing

In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse NLP tasks. Extensive research has explored how to enhance the logical reasoning abilities such as Chain-of-Thought, Chain-of-Thought with Self-Cons

Externí odkaz: http://arxiv.org/abs/2409.14051

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Survey of Computerized Adaptive Testing: A Machine Learning Perspective

Autor: Liu, Qi, Zhuang, Yan, Bi, Haoyang, Huang, Zhenya, Huang, Weizhe, Li, Jiatong, Yu, Junhao, Liu, Zirui, Hu, Zirui, Hong, Yuting, Pardos, Zachary A., Ma, Haiping, Zhu, Mengxiao, Wang, Shijin, Chen, Enhong

Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance. Widely adopted across diverse fields like education, healthc

Externí odkaz: http://arxiv.org/abs/2404.00712

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From Static Benchmarks to Adaptive Testing: Psychometrics in AI Evaluation

Autor: Zhuang, Yan, Liu, Qi, Ning, Yuting, Huang, Weizhe, Pardos, Zachary A., Kyllonen, Patrick C., Zu, Jiyun, Mao, Qingyang, Lv, Rui, Huang, Zhenya, Zhao, Guanhao, Zhang, Zheng, Wang, Shijin, Chen, Enhong

As AI systems continue to grow, particularly generative models like Large Language Models (LLMs), their rigorous evaluation is crucial for development and deployment. To determine their adequacy, researchers have developed various large-scale benchma

Externí odkaz: http://arxiv.org/abs/2306.10512

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Akademický článek

Understand how machine learning impact lung cancer research from 2010 to 2021: A bibliometric analysis

Autor: Chen Zijian, Liu Yangqi, Lin Zeying, Huang Weizhe

Publikováno v: Open Medicine, Vol 19, Iss 1, Pp 7-33 (2024)

Advances in lung cancer research applying machine learning (ML) technology have generated many relevant literature. However, there is absence of bibliometric analysis review that aids a comprehensive understanding of this field and its progress. Pres

Externí odkaz: https://doaj.org/article/6734fdc49ee8414dbcea66c44c152b23