Stylistic Evolution and LLM Neutrality in Singlish Language
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The paper introduces a training‑free method for uncovering prompt‑conditional stylistic axes in large language models (LLMs). By repeatedly sampling completions of a single prompt at high temperature and applying Principal Component Analysis (PCA) to the pooled hidden activations, the authors automatically label the resulting axes using the extreme (pole) generations. Validation against 245 human‑elicited stylistic annotations shows that, for the Qwen‑3.5‑4B‑Instruct model, the top two axes align with human dimensions with 72.8% precision and 43.6% macro‑recall, and 75.6% of validity ratings confirm the axes’ polar generations, while other models exhibit varying degrees of discoverability.
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arXiv:2609.22112v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated the ability to generate user-specific text with high stylistic fidelity. However, the personal data that...
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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.
arXiv:2609. 28245v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong performance in creative text generation, yet their ability to produce culturally grounded and stylistically constrained literary forms remains underexplored.