Safe Language Generation in the Limit

ACL ARR 2026 January Submission3925 Authors

Keywords: language learning in the limit, computational learning theory

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.

Paper Type: Long

Research Area: Linguistic theories, Cognitive Modeling and Psycholinguistics

Research Area Keywords: computational learning, learning in the limit, safety

Contribution Types: Theory

Languages Studied: Theoretical

Submission Number: 3925