Zobrazeno 1 - 10
of 962
pro vyhledávání: '"LI Zhitao"'
This paper introduces a novel privacy-preservation framework named PFID for LLMs that addresses critical privacy concerns by localizing user data through model sharding and singular value decomposition. When users are interacting with LLM systems, th
Externí odkaz:
http://arxiv.org/abs/2406.12238
Autor:
Ouyang, Sheng, Wang, Jianzong, Zhang, Yong, Li, Zhitao, Liang, Ziqi, Zhang, Xulong, Cheng, Ning, Xiao, Jing
Extractive Question Answering (EQA) in Machine Reading Comprehension (MRC) often faces the challenge of dealing with semantically identical but format-variant inputs. Our work introduces a novel approach, called the ``Query Latent Semantic Calibrator
Externí odkaz:
http://arxiv.org/abs/2404.19316
Autor:
Li, Ming, Zhang, Yong, He, Shwai, Li, Zhitao, Zhao, Hongyu, Wang, Jianzong, Cheng, Ning, Zhou, Tianyi
Instruction tuning is critical to improve LLMs but usually suffers from low-quality and redundant data. Data filtering for instruction tuning has proved important in improving both the efficiency and performance of the tuning process. But it also lea
Externí odkaz:
http://arxiv.org/abs/2402.00530
Autor:
Liao, Congyu, Cao, Xiaozhi, Iyer, Siddharth Srinivasan, Schauman, Sophie, Zhou, Zihan, Yan, Xiaoqian, Chen, Quan, Li, Zhitao, Wang, Nan, Gong, Ting, Wu, Zhe, He, Hongjian, Zhong, Jianhui, Yang, Yang, Kerr, Adam, Grill-Spector, Kalanit, Setsompop, Kawin
Publikováno v:
Magnetic Resonance in Medicine 2023
Purpose: This study aims to develop a high-resolution whole-brain multi-parametric quantitative MRI approach for simultaneous mapping of myelin-water fraction (MWF), T1, T2, and proton-density (PD), all within a clinically feasible scan time. Methods
Externí odkaz:
http://arxiv.org/abs/2312.13523
The Retrieval Question Answering (ReQA) task employs the retrieval-augmented framework, composed of a retriever and generator. The generator formulates the answer based on the documents retrieved by the retriever. Incorporating Large Language Models
Externí odkaz:
http://arxiv.org/abs/2310.18347
Recommender systems is set up to address the issue of information overload in traditional information retrieval systems, which is focused on recommending information that is of most interest to users from massive information. Generally, there is a se
Externí odkaz:
http://arxiv.org/abs/2308.15701
Autor:
Li, Ming, Zhang, Yong, Li, Zhitao, Chen, Jiuhai, Chen, Lichang, Cheng, Ning, Wang, Jianzong, Zhou, Tianyi, Xiao, Jing
In the realm of Large Language Models (LLMs), the balance between instruction data quality and quantity is a focal point. Recognizing this, we introduce a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-so
Externí odkaz:
http://arxiv.org/abs/2308.12032
Autor:
Fan, Jiaxin, Zhang, Yong, Li, Hanzhang, Wang, Jianzong, Li, Zhitao, Ouyang, Sheng, Cheng, Ning, Xiao, Jing
Chinese Automatic Speech Recognition (ASR) error correction presents significant challenges due to the Chinese language's unique features, including a large character set and borderless, morpheme-based structure. Current mainstream models often strug
Externí odkaz:
http://arxiv.org/abs/2308.03423
Conversational Question Answering (CQA) is a challenging task that aims to generate natural answers for conversational flow questions. In this paper, we propose a pluggable approach for extractive methods that introduces a novel prompt-guided copy me
Externí odkaz:
http://arxiv.org/abs/2308.03422
Publikováno v:
Zhongguo quanke yixue, Vol 28, Iss 02, Pp 183-192 (2025)
Background Diabetes mellitus is a global public health issue. Cross sectional studies have found that visceral fat is closely related to the prevalence of diabetes mellites, while prospective cohort studies on the trend of onset time of diabetes mell
Externí odkaz:
https://doaj.org/article/4dc272cac463462387e5856e940d370d
