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

• URL: http://arxiv.org/abs/2409.16383v4
• Date: Tue, 17 Dec 2024 17:42:18 GMT
• Title: RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation
• Authors: Ioannis Panagiotopoulos, Giorgos Filandrianos, Maria Lymperaiou, Giorgos Stamou,
• Abstract summary: This paper explores how different prompting techniques impact performance on riddles that demand diverse reasoning skills. We introduce RISCORE, a fully automated prompting method that generates and utilizes contextually reconstructed sentence-based puzzles. Our experiments demonstrate that RISCORE significantly improves the performance of language models in both vertical and lateral thinking tasks.
• Score: 1.9939549451457024
• License:
• 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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