Error Analysis in the Generative Artificial Intelligence Era and Research on the Turning of"Old Road"to"New Road"
With the advent of the generative AI era,language bias analysis is undergoing a transformation,offering both the opportunity and necessity for it to expand from concentrating on errors in human language acquisition to encompassing biases in generated text.Traditional bias analysis has primarily addressed errors resulting from human cognitive limitations,whereas biases in the generative AI era originate from algorithmic logic and technological constraints.Consequently,bias analysis must reshape its categorization of errors,emphasizing new issues in machine-generated content,employing interdisciplinary methods to explore underlying mechanisms,and constructing analytical frameworks adapted to this new environment.In light of the novel biases introduced by generative AI texts,including randomness,insufficient cultural adaptability,and context comprehension deviations,research methodologies in bias analysis are transitioning from a purely linguistic foundation towards an integration of deep learning and big data-driven paradigms.Future studies must innovate in bias identification and correction strategies,consolidating knowledge from multiple fields,and developing efficient techniques for detecting,rectifying,and preventing biases in generative AI text.This advancement aims to propel the enhancement of AI language generation system quality and foster its healthy development.
generative artificial intelligenceerror analysisgenerative text errortransition
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