The Attribution Contract for Generative Language Models
Feature attribution scores each part of an input by how much it explains a model's output. We argue that in generative language models these scores carry no fixed meaning. A classifier has a single output to explain, but a generative model produces its output token by token, and each generated token is both an output and an input, so explaining the output becomes several distinct questions. We support this claim by proposing the Attribution Contract, a framework that names the question a set of attribution scores answers. A contract specifies the model score being explained, what is held fixed, the target output, the generation process, and which features can receive attribution, choices ...
| Authors | Giang Nguyen |
|---|---|
| Primary category | cs.LG |
| Published | 21 May 2026 |
| Last updated at source | 26 August 2026 |
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