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This paper presents an empirical study adapting the small language model Phi Silica for short-form text rewriting through dataset curation, prompt distillation, and parameter-efficient fine-tuning, showing that targeted adaptation significantly improves semantic fidelity and reduces hallucinations.
This paper reveals that commercial AI detectors like GPTZero and Pangram judge text from base language models as overwhelmingly human, while instruction-tuned model outputs are flagged as AI-generated. The authors propose HIP, a detector-agnostic iterative paraphrasing pipeline that improves human-likeness while preserving semantics.
A research paper finds that base language models appear human to AI detectors, unlike instruction-tuned models. The authors propose a paraphrasing pipeline (HIP) that improves human-likeness while preserving semantics across model sizes.