# A Survey on LLM-generated Text Detection: Necessity, Methods, and Future Directions

**Authors:** Junchao Wu, Shu Yang, Runzhe Zhan, Yulin Yuan, Derek F. Wong, Lidia S. Chao  
**arXiv:** [2310.14724](https://arxiv.org/abs/2310.14724)  
**Date:** 2023-10-23  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2310.14724v2/

## Key points

- Large Language Models (LLMs) have revolutionized complex language interaction
- Detecting LLM-generated text is crucial to prevent misuse and protect realms like artistic expression and social networks
- The survey collates recent research breakthroughs in LLM-generated text detection and emphasizes the need for further research
- Existing datasets used for this task have limitations and require development
- Different paradigms for LLM-generated text detection are discussed, addressing challenges such as out-of-distribution problems, potential attacks, and data ambiguity
- Responsible AI implementation is highlighted as important, with directions for future research provided
- Potential datasets for LLM-generated text detection cover various domains such as news, politics, sports, biomedical science, etc.
- A methodology specifically designed for detecting LLM-generated text is presented along with challenges and prospective directions for future research in text generation for LLMs
- 82 relevant pieces of literature were identified through de-duplication and manual screening; most were published in 2023 indicating vibrant development in the field
- The survey provides synthesis and analysis of data related to LLM-generated text detection techniques, primary detectors used, evaluation metrics employed, issues faced, and future research directions
- Information about the CHEAT dataset as a valuable resource for LLM-generated text detection tasks is included.

## Summary

The field of Large Language Models (LLMs) has revolutionized the way we interact with complex language. LLM-generated text is now prevalent in various aspects of our daily lives, and it is crucial to develop detectors that can identify such text. Detecting LLM-generated text is essential to prevent misuse and protect realms like artistic expression and social networks from harmful influence. In this survey, the authors collate recent research breakthroughs in LLM-generated text detection and emphasize the need for further detector research. They also analyze existing datasets used for this task, highlighting their limitations and developmental requirements. The survey delves into different paradigms for LLM-generated text detection, addressing challenges such as out-of-distribution problems, potential attacks, and data ambiguity. The authors highlight the importance of responsible artificial intelligence (AI) implementation and provide interesting directions for future research in LLM-generated text detection. The aim of this survey is to offer a comprehensive introduction to newcomers while providing seasoned researchers with valuable updates in this field. Additionally, the authors provide a list of potential datasets that can be extended for LLM-generated text detection tasks. These datasets cover various domains such as news, politics, sports, biomedical science, question answering, story generation, climate change tweets, opinion statements, academic writing, and more. The article also presents a methodology specifically designed for detecting LLM-generated text and discusses challenges and prospective directions for future research in the domain of text generation for LLMs. It articulates the necessity and applications of LLM-generated text detection. Through a process of de-duplication and manual screening 82 relevant pieces of literature were identified; the majority of these works were published in 2023 indicating the vibrant development within this field. Overall this survey provides a synthesis and analysis of data related to LLM-generated text detection techniques; primary detectors used in research studies; evaluation metrics employed in assessing detector performance; issues faced in this domain; and future research directions. The provided context also includes information about the CHEAT dataset which is a valuable resource for LLM-generated text detection tasks.