Authors: Junchao Wu, Shu Yang, Runzhe Zhan, Yulin Yuan, Derek F. Wong, Lidia S. Chao

Published on: October 23, 2023

Impact Score: 8.0

Arxiv code: Arxiv:2310.14724

Summary

• What is new: Collation of recent research breakthroughs in LLM-generated text detection and highlighting the need for enhanced detector research.
• Why this is important: The need to develop detectors to identify text generated by large language models (LLMs) to prevent potential misuse.
• What the research proposes: Survey of advancements in watermarking, statistics-based, neural-based detectors, and human-assisted methods for detecting LLM-generated text.
• Results: Outlined the limitations of current datasets, analyzed challenges in detection paradigms, and pointed out directions for future research to implement responsible AI.

Technical Details

Technological frameworks used: Not specified

Models used: Watermarking techniques, statistics-based detectors, neural-based detectors, human-assisted methods.

Data used: Prevalent datasets discussed with their limitations.

Potential Impact

Social networks, artistic communities, and any sector relying on content authenticity could benefit or be disrupted.

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