Human and Automatic Detection of Generated Text

15 citations recorded by the source corpus.

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Towards a Human-like Open-Domain Chatbot (2020, background): “Many works, e.g., Zhao et al. (2017); Xu et al. (2018); Ippolito et al. (2019b), have focused solely on the diversity aspect to counter the commonly observed problem of models generating generic responses (Li et al., 2016a).”
Limits of Detecting Text Generated by Large-Scale Language Models (2020, background): “Several recent studies have used supervised learning to develop classifiers for this task [9], [10], [15]–[17] and interpreted their properties.”
Trading Off Diversity and Quality in Natural Language Generation (2020, methodology): “The concept of oversampling generations and then ranking them has been popular since the days of statistical machine translation (Shen et al., 2004) but has also been used more recently in other domains (Li et al., 2016; Ippolito et al., 2019b; Kriz et al., 2019).”
Do Language Models Plagiarize? (2022, background): “Given that GPT-2 relies on the probability distribution when generating word-tokens, there exist various decoding methods which are well known to be critical for performance in text generation [24].”