Truth as a Compression Artifact in Language Model Training
Why do language models trained on contradictory data prefer correct answers? In controlled experiments with small transformers (3.5M--86M parameters), we show that this preference tracks the compressibility structure of errors rather than truth per se. We train GPT-2 style models on corpora where each mathematical problem appears with both correct and incorrect solutions -- a denoising design that directly models conflicting information about the same fact. When errors are random, models extract the correct signal with accuracy scaling from 65% to 85% with model size. When errors follow a coherent alternative rule system, accuracy drops to chance (~45--51%): the model cannot distinguish t...
| Authors | Konstantin Krestnikov |
|---|---|
| Primary category | cs.CL |
| Published | 12 March 2026 |
| Last updated at source | 3 April 2026 |
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