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Automatic Detection of Machine Generated Text: A Critical Survey

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arxiv 2011.01314 v1 pith:UMQGYQIG submitted 2020-11-02 cs.CL cs.AI

Ganesh Jawahar , Muhammad Abdul-Mageed , Laks V.S. Lakshmanan This is my paper · ORCID
classification cs.CLcs.AI
keywords texttgmsworkcriticaldetectorsfakegeneratedhuman
verification ladder T0 review T1 audit T2 compute T3 formal

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Text generative models (TGMs) excel in producing text that matches the style of human language reasonably well. Such TGMs can be misused by adversaries, e.g., by automatically generating fake news and fake product reviews that can look authentic and fool humans. Detectors that can distinguish text generated by TGM from human written text play a vital role in mitigating such misuse of TGMs. Recently, there has been a flurry of works from both natural language processing (NLP) and machine learning (ML) communities to build accurate detectors for English. Despite the importance of this problem, there is currently no work that surveys this fast-growing literature and introduces newcomers to important research challenges. In this work, we fill this void by providing a critical survey and review of this literature to facilitate a comprehensive understanding of this problem. We conduct an in-depth error analysis of the state-of-the-art detector and discuss research directions to guide future work in this exciting area.

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Cited by 6 Pith papers

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