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arXiv Machine Learning By Aniket Anand, Janvijay Singh, Zhewei Sun, Dilek Hakkani-T\"ur, Nick Feamster

Measuring, Localizing, and Reducing the AI-Like Writing Style of Post-Trained LLMs

Read the original on arXiv Machine Learning →

The paper investigates how post‑training of language models introduces or amplifies an AI‑like writing style and whether this style has a localized internal signature. By comparing human text, base‑model outputs, and aligned‑model outputs with matched human‑source prefixes, the authors find that aligned generations are less similar to human corpora and more easily detected as AI‑like. They propose PASTA, a training‑free method that estimates a post‑training alignment signature from residual contrasts and ablates the corresponding direction during decoding, reducing detection rates across multiple models and detectors while preserving relevance and coherence.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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