Safe Language Generation in the Limit
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915726432927744 |
|---|---|
| author | Anastasopoulos, Antonios Ateniese, Giuseppe Kornaropoulos, Evgenios M. |
| author_facet | Anastasopoulos, Antonios Ateniese, Giuseppe Kornaropoulos, Evgenios M. |
| contents | Recent results in learning a language in the limit have shown that, although language identification is impossible, language generation is tractable. As this foundational area expands, we need to consider the implications of language generation in real-world settings.
This work offers the first theoretical treatment of safe language generation. Building on the computational paradigm of learning in the limit, we formalize the tasks of safe language identification and generation. We prove that under this model, safe language identification is impossible, and that safe language generation is at least as hard as (vanilla) language identification, which is also impossible. Last, we discuss several intractable and tractable cases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_08648 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Safe Language Generation in the Limit Anastasopoulos, Antonios Ateniese, Giuseppe Kornaropoulos, Evgenios M. Computation and Language Machine Learning Recent results in learning a language in the limit have shown that, although language identification is impossible, language generation is tractable. As this foundational area expands, we need to consider the implications of language generation in real-world settings. This work offers the first theoretical treatment of safe language generation. Building on the computational paradigm of learning in the limit, we formalize the tasks of safe language identification and generation. We prove that under this model, safe language identification is impossible, and that safe language generation is at least as hard as (vanilla) language identification, which is also impossible. Last, we discuss several intractable and tractable cases. |
| title | Safe Language Generation in the Limit |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2601.08648 |