Computer-Generated Text Detection Using Machine Learning: A Systematic Review

By Daria Beresneva. 2016

54 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Mohit Iyyer
2. Sebastian Gehrmann
3. John Wieting
4. Kalpesh Krishna
5. Graham Neubig
6. Yonatan Belinkov
7. Alexander M. Rush
8. Mohit Bansal

In Evaluation & Benchmarks

1. Preslav Nakov
2. John Wieting
3. Terry Yue Zhuo
4. Mohit Iyyer
5. Alexander M. Rush
6. Kalpesh Krishna
7. Hendrik Strobelt
8. Sebastian Gehrmann

Citations in the corpus

• GLTR: Statistical Detection and Visualization of Generated Text (2019, background): “Similar work finds that texts differ in perplexity under a language model (Beresneva, 2016), frequency of rare bigrams (Grechnikov et al.”
• DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text (2023, background): “Another stream of work attempts to distinguish machine-generated from human-written texts based on statistical irregularities in the entropy (Lavergne et al., 2008), perplexity (Beresneva, 2016) or in the n -gram frequencies (Badaskar et al., 2008).”
• Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense (2023, methodology): “Early methods detect statistical irregularities in entropy [Lavergne et al., 2008] and perplexity [Beresneva, 2016], while Gehrmann et al. [2019] introduced the GLTR visualizer to assist humans in detecting AI-generated text.”