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arXiv · 2409.16383

RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation

Ioannis Panagiotopoulos·Giorgos Filandrianos·Maria Lymperaiou·Giorgos Stamou
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Abstract

Riddle-solving requires advanced reasoning skills, pushing LLMs to engage in abstract thinking and creative problem-solving, often revealing limitations in their cognitive abilities. In this paper, we examine the riddle-solving capabilities of LLMs using a multiple-choice format, exploring how different prompting techniques impact performance on riddles that demand diverse reasoning skills. To enhance results, we introduce RISCORE (RIddle Solving with COntext REcontruciton) a novel fully automated prompting method that generates and utilizes contextually reconstructed sentence-based puzzles in conjunction with the original examples to create few-shot exemplars. Our experiments demonstrate that RISCORE significantly improves the performance of language models in both vertical and lateral thinking tasks, surpassing traditional exemplar selection strategies across a variety of few-shot settings.

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Ioannis Panagiotopoulos, Giorgos Filandrianos, Maria Lymperaiou, Giorgos Stamou. 2024-12-17. RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation. https://arxiv.org/abs/2409.16383

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