Authors: Dmytro Valiaiev
Published on: January 02, 2024
Impact Score: 8.45
Arxiv code: Arxiv:2402.01642
Summary
- What is new: This survey provides a comprehensive overview of the developments in identifying and mitigating the issues arising from machine-generated text, focusing on recent advancements in natural language generation and generative pre-trained transformers.
- Why this is important: The challenge of distinguishing between text generated by humans and advanced language models, due to their increased sophistication and the consequent oversupply of machine-generated content in the public domain.
- What the research proposes: Implementing automated systems and possibly reverse-engineered language models to empower human agents to differentiate between artificial and human-created texts, while also considering the socio-technological impacts of these technologies.
- Results: The paper synthesizes accomplishments in the field and explores the societal implications of machine-generated texts, aiming to foster the development of methods to address the challenges posed by the use of advanced language models.
Technical Details
Technological frameworks used: Natural Language Generation (NLG), Generative Pre-trained Transformer (GPT) models.
Models used: Reverse-engineered language models for text distinction.
Data used: Machine-generated text trends and examples.
Potential Impact
Journalism, customer service, and academic sectors could be disrupted, while companies developing NLG and GPT models could benefit.
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