The Science of Detecting LLM-Generated Text

Recent advancements in natural language generation (NLG) technology have significantly improved the diversity, control, and quality of large language models (LLM)-generated text. A notable example is OpenAI's ChatGPT, which demonstrates exceptional performance in tasks such as answering questions, composing email messages, essays, and codes. However, this newfound capability to produce human-like text at high efficiency also raises concerns about detecting and preventing misuse of LLMs in tasks such as phishing, disinformation, and academic dishonesty. For instance, many schools banned ChatGPT due to concerns over cheating in assignments,11 and media outlets have raised the alarm over fake news generated by LLMs.14 These concerns about the misuse of LLMs have hindered the NLG application in important domains such as media and education.