right-parameters-not-more-parameters

Do We Really Need More Parameters, or the Right Parameters?

A Comparative Study of AI Text Humanization Across Model Scales

Rofati · April 2026

📄 Read the Paper (PDF)

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The prevailing assumption in LLM deployment is that larger models produce better outputs. We challenge this in the domain of AI text humanization — rewriting machine-generated text to read as naturally human-written.

We compare three models spanning a 48× parameter range — Qwen 2.5-1.5B with speculative decoding, Gemma 4 E4B at 4-bit quantization, and Qwen 2.5-72B at full precision — on five diverse AI-generated passages using identical prompts.

The 4.5B quantized model wins. It surpasses the 72B model by 62% in contraction usage, 66% in sentence structure variation, and achieves 100% AI pattern removal — while running on free CPU hardware.

We introduce the "Polished AI" trap: larger models are so fluent that their rewrites become more uniform and detectable than mid-scale counterparts.

Live Demos

• ⚡ Qwen 2.5-1.5B — Speculative Decoding
• 🧠 Gemma 4 E4B — Q4 GGUF + Chain-of-Thought

Citation

@article{rofati2026rightparameters,
  title={Do We Really Need More Parameters, or the Right Parameters? 
         A Comparative Study of AI Text Humanization Across Model Scales},
  author={Rofati},
  year={2026},
  url={https://huggingface.co/Rofati/right-parameters-not-more-parameters}
}