Self-training restructures language by amplifying surface markers and collapsing deep syntax according to structural depth rather than frequency, as evidenced by correlations across multiple models and a human fine-tuning control.
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AI-generated text detectors achieve high benchmark accuracy by exploiting unstable dataset-specific linguistic features, as evidenced by cross-domain degradation and differing SHAP explanations across corpora.
Genre and model exert stronger influence on writing style than human/LLM source or decoding strategy in a broad comparison of lexicogrammatical features.
citing papers explorer
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Self-Training Doesn't Flatten Language -- It Restructures It: Surface Markers Amplify While Deep Syntax Dies
Self-training restructures language by amplifying surface markers and collapsing deep syntax according to structural depth rather than frequency, as evidenced by correlations across multiple models and a human fine-tuning control.
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Why AI-Generated Text Detection Fails: Evidence from Explainable AI Beyond Benchmark Accuracy
AI-generated text detectors achieve high benchmark accuracy by exploiting unstable dataset-specific linguistic features, as evidenced by cross-domain degradation and differing SHAP explanations across corpora.
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Interpretable Stylistic Variation in Human and LLM Writing Across Genres, Models, and Decoding Strategies
Genre and model exert stronger influence on writing style than human/LLM source or decoding strategy in a broad comparison of lexicogrammatical features.