2024 · 13 citations · 6 references
ComputingComputer ScienceArtificial IntelligenceComputational LinguisticsNatural Language ProcessingLanguage ModelingLanguage ModelsNatural Language GenerationText GenerationAutomated Text GenerationEvaluation Of Text GenerationLanguage GenerationMachine-generated Text DetectionPen InputRegulatory Scrutiny
Large Language Models (LLMs) have revolutionized the field of Natural Language Generation (NLG) by demonstrating an impressive ability to generate human-like text. However, their widespread usage introduces challenges that necessitate thoughtful examination, ethical scrutiny, and responsible practices. In this study, we delve into these challenges, explore existing strategies for mitigating them, with a particular emphasis on identifying AI-generated text as the ultimate solution. Additionally, we assess the feasibility of detection from a theoretical perspective and propose novel research directions to address the current limitations in this domain.
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GPT detectors are biased against non-native English writers
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On the Risk of Misinformation Pollution with Large Language Models
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