Transducing Language Models
Modern language models define distributions over strings, but downstream tasks often require different output formats. For instance, a model that generates byte-pair strings does not directly produce word-level predictions, and a DNA model does not directly produce amino-acid sequences. In such cases, a deterministic string-to-string transformation can convert the model's output to the desired form. This is a familiar pattern in probability theory: applying a function $f$ to a random variable $X\sim p$ yields a transformed random variable $f(X)$ with an induced distribution. While such transformations are occasionally used in language modeling, prior work does not treat them as yielding n...
| Authors | Vésteinn Snæbjarnarson, Samuel Kiegeland, Tianyu Liu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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