arXiv — NLP / Computation & Language · June 29, 2026 · 3 min read

Safe Language Generation in the Limit

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Computer Science > Computation and Language

arXiv:2601.08648 (cs)

[Submitted on 13 Jan 2026 (v1), last revised 26 Jun 2026 (this version, v2)]

Title:Safe Language Generation in the Limit

Authors:Antonios Anastasopoulos, Giuseppe Ateniese, Evgenios M. Kornaropoulos

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Abstract: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.

Subjects:	Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as:	arXiv:2601.08648 [cs.CL]
	(or arXiv:2601.08648v2 [cs.CL] for this version)
	https://doi.org/10.48550/arXiv.2601.08648 arXiv-issued DOI via DataCite

Submission history

From: Antonios Anastasopoulos [view email]
[v1] Tue, 13 Jan 2026 15:25:44 UTC (138 KB)
[v2] Fri, 26 Jun 2026 03:52:01 UTC (136 KB)

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