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The powerful ability to understand, follow, and generate complex languageemerging from large language models (LLMs) makes LLM-generated text flood manyareas of our daily lives at an incredible speed and is widely accepted byhumans. As LLMs continue to expand, there is an imperative need to developdetectors that can detect LLM-generated text. This is crucial to mitigatepotential misuse of LLMs and safeguard realms like artistic expression andsocial networks from harmful influence of LLM-generated content. TheLLM-generated text detection aims to discern if a piece of text was produced byan LLM, which is essentially a binary classification task. The detectortechniques have witnessed notable advancements recently, propelled byinnovations in watermarking techniques, zero-shot methods, fine-turning LMsmethods, adversarial learning methods, LLMs as detectors, and human-assistedmethods. In this survey, we collate recent research breakthroughs in this areaand underscore the pressing need to bolster detector research. We also delveinto prevalent datasets, elucidating their limitations and developmentalrequirements. Furthermore, we analyze various LLM-generated text detectionparadigms, shedding light on challenges like out-of-distribution problems,potential attacks, and data ambiguity. Conclusively, we highlight interestingdirections for future research in LLM-generated text detection to advance theimplementation of responsible artificial intelligence (AI). Our aim with thissurvey is to provide a clear and comprehensive introduction for newcomers whilealso offering seasoned researchers a valuable update in the field ofLLM-generated text detection. The useful resources are publicly available at:https://github.com/NLP2CT/LLM-generated-Text-Detection.

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