Non Verbis, Sed Rebus: Large Language Models are Weak Solvers of Italian Rebuses

Gabriele Sarti; Tommaso Caselli; Malvina Nissim; Arianna Bisazza

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arXiv · 2408.00584

Non Verbis, Sed Rebus: Large Language Models are Weak Solvers of Italian Rebuses

Gabriele Sarti·Tommaso Caselli·Malvina Nissim·Arianna Bisazza

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Abstract

Rebuses are puzzles requiring constrained multi-step reasoning to identify a hidden phrase from a set of images and letters. In this work, we introduce a large collection of verbalized rebuses for the Italian language and use it to assess the rebus-solving capabilities of state-of-the-art large language models. While general-purpose systems such as LLaMA-3 and GPT-4o perform poorly on this task, ad-hoc fine-tuning seems to improve models' performance. However, we find that performance gains from training are largely motivated by memorization. Our results suggest that rebus solving remains a challenging test bed to evaluate large language models' linguistic proficiency and sequential instruction-following skills.

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Gabriele Sarti, Tommaso Caselli, Malvina Nissim, Arianna Bisazza. 2024-08-01. Non Verbis, Sed Rebus: Large Language Models are Weak Solvers of Italian Rebuses. https://arxiv.org/abs/2408.00584

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