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pro vyhledávání: '"Xiao, Zeguan"'
The widespread applications of large language models (LLMs) have brought about concerns regarding their potential misuse. Although aligned with human preference data before release, LLMs remain vulnerable to various malicious attacks. In this paper,
Externí odkaz:
http://arxiv.org/abs/2407.01902
Extensive efforts have been made before the public release of Large language models (LLMs) to align their behaviors with human values. However, even meticulously aligned LLMs remain vulnerable to malicious manipulations such as jailbreaking, leading
Externí odkaz:
http://arxiv.org/abs/2403.08424
Graph-based Aspect-based Sentiment Classification (ABSC) approaches have yielded state-of-the-art results, expecially when equipped with contextual word embedding from pre-training language models (PLMs). However, they ignore sequential features of t
Externí odkaz:
http://arxiv.org/abs/2110.00171
Autor:
XIAO Zeguan, CHEN Qingliang
Publikováno v:
Jisuanji kexue yu tansuo, Vol 16, Iss 2, Pp 395-402 (2022)
The aim of aspect based sentiment analysis (ABSA) is to classify the sentiment polarity towards a particular aspect in a sentence. Existing approaches usually apply attention mechanism to modeling the connection between aspects and opinion expression
Externí odkaz:
https://doaj.org/article/8354c6c567024bd9820d18c96e35ea09