LINGOLY-TOO: Disentangling Memorisation from Reasoning with Linguistic
  Templatisation and Orthographic Obfuscation
Authors: name: Jude Khouja, name: Karolina Korgul, name: Simi Hellsten, name: Lingyi Yang, name: Vlad Neacs, name: Harry Mayne, name: Ryan Kearns, name: Andrew Bean, name: Adam Mahdi

Effective evaluation of the reasoning capabilities of large language models
(LLMs) are susceptible to overestimation due to data exposure of evaluation
benchmarks. We introduce a framework for producing linguistic reasoning
problems that reduces the effect of memorisation in model performance estimates
and apply this framework to develop LINGOLY-TOO, a challenging evaluation
benchmark for linguistic reasoning. By developing orthographic templates, we
dynamically obfuscate the writing systems of real languages to generate
numerous question variations. These variations preserve the reasoning steps
required for each solution while reducing the likelihood of specific problem
instances appearing in model training data. Our experiments demonstrate that
frontier models, including OpenAI o1-preview and DeepSeem R1, struggle with
advanced reasoning. Our analysis also shows that LLMs exhibit noticeable
variance in accuracy across permutations of the same problem, and on average
perform better on questions appearing in their original orthography. Our
findings highlight the opaque nature of response generation in LLMs and provide
evidence that prior data exposure contributes to overestimating the reasoning
capabilities of frontier models.