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language model-based detectors
Language model-based detectors are automated computational systems designed to determine whether a given text was produced by artificial intelligence or composed by a human by leveraging the architecture, representations, or statistical properties of language models. These systems generally operate either as fine-tuned neural classifiers trained on labeled datasets of human and machine-generated writing or by evaluating predictive statistical signals such as token probabilities, perplexity, and structural regularity. They are commonly applied to maintain academic integrity, evaluate content authenticity, and curb the spread of automated misinformation, though their accuracy can vary depending on text length, stylistic modifications, domain shifts, and the rapid evolution of generative models.
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