RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation
<|reference_start|>RISCORE: Enhancing In-Context Riddle Solving in Language Models through Context-Reconstructed Example Augmentation: 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.<|reference_end|>
@article{panagiotopoulos2024riscore:,
    title={RISCORE: Enhancing In-Context Riddle Solving in Language Models through
  Context-Reconstructed Example Augmentation},
    author={Ioannis Panagiotopoulos, Giorgos Filandrianos, Maria Lymperaiou,
  Giorgos Stamou},
    journal={arXiv preprint arXiv:2409.16383},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16383},
    primaryClass={cs.CL}
}