arXiv Machine Learning By Ziyi Cai, Shuangping Li, Yiheng Shen, Kangning Wang, Peng Zhang

Dense Language Generation Made Simple: Deterministic, Randomized, and Multi-Order Algorithms

Read the original on arXiv Machine Learning →

arXiv:2608. 01320v1 Announce Type: cross Abstract: Language generation in the limit is a theoretical framework for studying how a generator can learn to produce new valid strings from a stream of positive examples.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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
Jul 28

Hallucination Rates in Language Generation

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.

By Debmalya Panigrahi, Fan Wei, Ian Zhang
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
Jul 15

Language Identification with Succinct Machine-Independent Traces

arXiv:2607. 12443v1 Announce Type: cross Abstract: Motivated by the power of large language models, there has been renewed interest in the Gold-Angluin model of language identification in the limit, with an eye toward variants of the model that might overcome the negative results for its original formulation.

By Moses Charikar, Jon Kleinberg, Chirag Pabbaraju