Summary of Detection Of Machine-generated Text: Literature Survey, by Dmytro Valiaiev
Detection of Machine-Generated Text: Literature Survey
by Dmytro Valiaiev
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: Language models have become so advanced that they can produce fake text that is indistinguishable from human-written content. This has led to concerns about the impact of these models on society, including issues with journalism, customer service, and academia. To mitigate these risks, it’s essential to develop systems that can detect machine-generated text and ensure a balanced approach to using these technologies. This literature survey aims to synthesize existing research in natural language generation (NLG) and generative pre-trained transformer (GPT) models, while exploring the societal implications of these breakthroughs. The study also highlights the importance of understanding the capabilities and limitations of language models to develop robust approaches for resolving issues connected with machine-generated text. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Language models can create fake text that is very convincing. This has led to concerns about how these models are changing society, including issues in journalism, customer service, and education. To fix this problem, we need to develop systems that can tell the difference between human-written text and machine-generated text. This study looks at existing research on language models and their impact on society. It also explores the benefits and drawbacks of using these technologies. |
Keywords
* Artificial intelligence * Gpt * Transformer
