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    <abstract>Riddle-solving requires advanced reasoning skills, pushing Large Language Models (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.</abstract>
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%0 Conference Proceedings
%T RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation
%A Panagiotopoulos, Ioannis
%A Filandrianos, George
%A Lymperaiou, Maria
%A Stamou, Giorgos
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F panagiotopoulos-etal-2025-riscore
%X Riddle-solving requires advanced reasoning skills, pushing Large Language Models (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.
%U https://aclanthology.org/2025.coling-main.633/
%P 9431-9455

Markdown (Informal)

[RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation](https://aclanthology.org/2025.coling-main.633/) (Panagiotopoulos et al., COLING 2025)

• RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation (Panagiotopoulos et al., COLING 2025)

ACL

• Ioannis Panagiotopoulos, George Filandrianos, Maria Lymperaiou, and Giorgos Stamou. 2025. RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation. In Proceedings of the 31st International Conference on Computational Linguistics, pages 9431–9455, Abu Dhabi, UAE. Association for Computational Linguistics.