Zobrazeno 1 - 10
of 18 115
pro vyhledávání: '"Yu, Zhou"'
Alignment is a crucial step to enhance the instruction-following and conversational abilities of language models. Despite many recent work proposing new algorithms, datasets, and training pipelines, there is a lack of comprehensive studies measuring
Externí odkaz:
http://arxiv.org/abs/2407.06542
Coherence in writing, an aspect that second-language (L2) English learners often struggle with, is crucial in assessing L2 English writing. Existing automated writing evaluation systems primarily use basic surface linguistic features to detect cohere
Externí odkaz:
http://arxiv.org/abs/2406.19650
Dialogue systems have been used as conversation partners in English learning, but few have studied whether these systems improve learning outcomes. Student passion and perseverance, or grit, has been associated with language learning success. Recent
Externí odkaz:
http://arxiv.org/abs/2406.17982
Autor:
Qian, Kun, Wan, Shunji, Tang, Claudia, Wang, Youzhi, Zhang, Xuanming, Chen, Maximillian, Yu, Zhou
As large language models achieve impressive scores on traditional benchmarks, an increasing number of researchers are becoming concerned about benchmark data leakage during pre-training, commonly known as the data contamination problem. To ensure fai
Externí odkaz:
http://arxiv.org/abs/2406.17681
Autor:
Horvitz, Zachary, Patel, Ajay, Singh, Kanishk, Callison-Burch, Chris, McKeown, Kathleen, Yu, Zhou
The goal of text style transfer is to transform the style of texts while preserving their original meaning, often with only a few examples of the target style. Existing style transfer methods generally rely on the few-shot capabilities of large langu
Externí odkaz:
http://arxiv.org/abs/2406.15586
Aiming at the limitations of traditional medical decision system in processing large-scale heterogeneous medical data and realizing highly personalized recommendation, this paper introduces a personalized medical decision algorithm utilizing graph ne
Externí odkaz:
http://arxiv.org/abs/2405.17460
This paper introduces an innovative multi-modal fusion deep learning approach to overcome the drawbacks of traditional single-modal recognition techniques. These drawbacks include incomplete information and limited diagnostic accuracy. During the fea
Externí odkaz:
http://arxiv.org/abs/2406.18546
Autor:
Shao, Zhenwei, Yu, Zhou, Yu, Jun, Ouyang, Xuecheng, Zheng, Lihao, Gai, Zhenbiao, Wang, Mingyang, Ding, Jiajun
By harnessing the capabilities of large language models (LLMs), recent large multimodal models (LMMs) have shown remarkable versatility in open-world multimodal understanding. Nevertheless, they are usually parameter-heavy and computation-intensive,
Externí odkaz:
http://arxiv.org/abs/2405.12107
This paper combines Struts and Hibernate two architectures together, using DAO (Data Access Object) to store and access data. Then a set of dual-mode humidity medical image library suitable for deep network is established, and a dual-mode medical ima
Externí odkaz:
http://arxiv.org/abs/2404.18419
Unsupervised constrained text generation aims to generate text under a given set of constraints without any supervised data. Current state-of-the-art methods stochastically sample edit positions and actions, which may cause unnecessary search steps.
Externí odkaz:
http://arxiv.org/abs/2404.15877