Forecasting Corpus-Sampling Disagreement in Masked Text Reconstruction
If a second text corpus were collected from the same population, how much would a predictor's outputs change? We study this question for masked-text reconstruction using a smoothed predictor built from nearby token-pair counts and evaluated over the full 50,257-token GPT-2 vocabulary. Treating each source document as one observation, we derive a first-order formula for the expected disagreement between predictors fitted to two independent corpora. The formula accounts for variable document length, random context counts, and correlations across token offsets, and it can be evaluated from a separate reference corpus without access to either target corpus. On TinyStories, WikiText-103, and C...
| Authors | Hare Krishna |
|---|---|
| Primary category | cs.CL |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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