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.

翻译：近期在极限学习语言方面的研究结果表明，尽管语言识别是不可能的，但语言生成是可行的。随着这一基础领域的扩展，我们需要考虑语言生成在现实世界环境中的影响。本研究首次对安全语言生成进行了理论探讨。基于极限学习的计算范式，我们形式化了安全语言识别与生成的任务。我们证明在该模型下，安全语言识别是不可能的，而安全语言生成至少与（普通）语言识别一样困难，后者同样是不可能的。最后，我们讨论了若干不可处理与可处理的情形。