arXiv Machine Learning By Debmalya Panigrahi, Fan Wei, Ian Zhang

Hallucination Rates in Language Generation

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arXiv:2607. 23361v1 Announce Type: cross Abstract: Language generation in the limit is an elegant model introduced by Kleinberg and Mullainathan [KM24] to formally study language generation by an algorithm that learns solely based on example strings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Jun 30

Generating in the Limit with Infinitely Many Hallucinations

arXiv:2606. 28354v1 Announce Type: cross Abstract: The classic paradigm of language identification in the limit models learning as a game between an adversary, who reveals strings from an unknown target language, and a learner tasked with identifying that language.

By Irene Strauss, Alexandra Butoi, Ryan Cotterell
arXiv Machine Learning
Jun 25

Space-Efficient Language Generation in the Limit

arXiv:2606. 25777v1 Announce Type: cross Abstract: We initiate a resource-aware theory of \textit{language generation in the limit} under the minimal constraint of space efficiency.

By Nicolas Flammarion, Chirag Pabbaraju, Hristo Papazov, Miltiadis Stouras, Ola Svensson
arXiv Machine Learning
Sep 11

Characterizing Language Generation in the Limit: Finite Witnesses and a Separation-Width Hierarchy

The paper studies the problem of language generation in the limit, where a learner must produce valid unseen elements from any exhaustive positive presentation of an unknown infinite language. It establishes that generation is possible precisely when each target language admits a finite positive witness such that all targets activated by any finite sample share an infinite common intersection. The authors introduce a separation-width hierarchy to measure the size of compatible witnesses, showing that every level of the hierarchy occurs and that countable families admit singleton witnesses while more complex families require unbounded finite witnesses. The results are formalized and verified in Lean, with the development available on GitHub.

By Xiaoyu Li, Andi Han, Jiaojiao Jiang, Junbin Gao
arXiv Machine Learning
Jun 29

Safe Language Generation in the Limit

arXiv:2601. 08648v2 Announce Type: replace-cross Abstract: Recent results in learning a language in the limit have shown that, although language identification is impossible, language generation is tractable.

By Antonios Anastasopoulos, Giuseppe Ateniese, Evgenios M. Kornaropoulos