[Submitted on 30 Jan 2026 (v1), last revised 3 Nov 2026 (this version, v3)]
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.
Submission history
From: Mikael Møller Høgsgaard [view email]
[v1] Fri, 30 Jan 2026 18:26:28 UTC (22 KB)
[v2] Wed, 22 Apr 2026 02:48:34 UTC (22 KB)
[v3] Tue, 3 Nov 2026 10:55:47 UTC (35 KB)
