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This arXiv paper is a theoretical learning-theory study of language identification and generation, the setting where a learner must identify or produce strings from an unknown language in a known collection. Prior results assumed the data always comes from some language in the collection (realizability); this work drops that assumption and proposes agnostic objectives with nearly tight rates. For AI and ML readers, the value is in the formal characterizations of what is learnable when models face out-of-collection or arbitrary data, which bears on the foundations of LLM-style generation and generalization guarantees, not on any immediate product.
Agnostic Language Identification and Generation
arXiv:2601.23258v3 Announce Type: replace Abstract: Recent works on language identification and generation have established tight statistical rates at which these tasks can be achieved. These works typically operate under a strong realizability assumption: that the input data is drawn from an unknown distribution necessarily supported on some language in a given collection. In this work, we relax this assumption of realizability entirely, and impose no restrictions on the distribution of the input data. We propose objectives to study both language identification and generation in this more general "agnostic" setup. Across both problems, we obtain novel interesting characterizations and nearly tight rates.
Abstract:Recent works on language identification and generation have established tight statistical rates at which these tasks can be achieved. These works typically operate under a strong realizability assumption: that the input data is drawn from an unknown distribution necessarily supported on some language in a given collection. In this work, we relax this assumption of realizability entirely, and impose no restrictions on the distribution of the input data. We propose objectives to study both language identification and generation in this more general "agnostic" setup. Across both problems, we obtain novel interesting characterizations and nearly tight rates.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
arXiv:2601.23258 [cs.LG]
(or arXiv:2601.23258v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2601.23258 Focus to learn more arXiv-issued DOI via DataCite
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