Statistics are often the only evidence of whether a text in an unread script records a language, but meaningless text shares many of them. A test means little until it is run on texts whose answer is known. We build such tests and apply them to the Voynich manuscript, whose text may be meaningless copying or a language hidden by a cipher. We write both accounts as programs, fit them with the same search budget and check them on held-out statistics. The copying process of Timm and Schinner predicts these statistics better than any cipher with one key, but ciphers with two keys, or that encode only the word classes of a real text, do equally well. A trace of language that needs no decipherment says more. In 28 texts in 16 natural languages the same word pairs keep sharing a line, and the manuscript shows this at half the strength of the weakest. Word codes fall to this level only with heavy miscopying or a plaintext whose word pairs recur weakly, such as abbreviated Latin. Solvers that read enciphered controls find nothing readable in the manuscript under letter ciphers, word codes or codes with one word per letter or syllable. One cipher remains, a nomenclator with two keys, which writes frequent words whole and spells the rest, and whose scribe miscopies more than half of the words. Our tests exclude it in all languages but Arabic and Hebrew. Planted messages show that a short message could still hide.
Hao Xu Jilin University, Rite Bo, Fausto Giunchiglia, Yingji Li, Rui Song Jilin University
原文摘要
Although studies have demonstrated that Large Language Models (LLMs) can perform well on Out-of-Distribution (OOD) tasks, their advantage tends to diminish as the distribution shift becomes more severe. Consequently, researchers aim to retrieve distributionally similar and informative demonstrations from the available source domain to boost the inference capabilities of LLMs. However, in practical scenarios where the target domain is inaccessible, evaluating the unknown distribution is challenging, which indirectly impacts the quality of the selected demonstrations. To address this problem, we propose DOPA, a demonstration search framework that incorporates an OOD proxy to approximate the inaccessible target domain and guide the retrieval process. Building on proxy-based evaluation, DOPA further introduces a Mahalanobis distance-based global diversity constraint to ensure sufficient diversity among the retrieved demonstrations. Experimental results on multiple LLMs and tasks demonstrate that DOPA effectively enhances robustness in OOD settings.
cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表