Detection of Machine-Generated Text: Literature Survey
Dmytro Valiaiev
Since language models produce fake text quickly and easily, there is an
oversupply of such content in the public domain. The degree of sophistication
and writing style has reached a point where differentiating between human
authored and machine-generated content is nearly impossible. As a result, works
generated by language models rather than human authors have gained significant
media attention and stirred controversy.Concerns regarding the possible
influence of advanced language models on society have also arisen, needing a
fuller knowledge of these processes. Natural language generation (NLG) and
generative pre-trained transformer (GPT) models have revolutionized a variety
of sectors: the scope not only permeated throughout journalism and customer
service but also reached academia. To mitigate the hazardous implications that
may arise from the use of these models, preventative measures must be
implemented, such as providing human agents with the capacity to distinguish
between artificially made and human composed texts utilizing automated systems
and possibly reverse-engineered language models. Furthermore, to ensure a
balanced and responsible approach, it is critical to have a full grasp of the
socio-technological ramifications of these breakthroughs. This literature
survey aims to compile and synthesize accomplishments and developments in the
aforementioned work, while also identifying future prospects. It also gives an
overview of machine-generated text trends and explores the larger societal
implications. Ultimately, this survey intends to contribute to the development
of robust and effective approaches for resolving the issues connected with the
usage and detection of machine-generated text by exploring the interplay
between the capabilities of language models and their possible implications.