The Truncation Blind Spot: How Decoding Strategies Systematically Exclude Human-Like Token Choices
Why does machine-generated text remain detectable? We investigate a mechanistic explanation at the decoding stage: standard strategies such as top-$k$ and nucleus sampling restrict generation to high-probability tokens, while human writers routinely choose contextually appropriate words from deeper in the model's probability distribution. Truncation makes a measurable share of these choices unreachable; we call this the \emph{truncation blind spot}. Across five open models and three domains, 8--18\% of human-selected tokens fall outside common truncation boundaries. Linguistic analysis further reveals disproportionate exclusion of content-word tokens. In a benchmark comprising 1.8 million...
| Authors | Esteban Garces Arias, Nurzhan Sapargali, Christian Heumann et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 March 2026 |
| Last updated at source | 23 September 2026 |
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