ParallelPARC: A Scalable Pipeline for Generating Natural-Language Analogies
<|reference_start|>ParallelPARC: A Scalable Pipeline for Generating Natural-Language Analogies: Analogy-making is central to human cognition, allowing us to adapt to novel situations -- an ability that current AI systems still lack. Most analogy datasets today focus on simple analogies (e.g., word analogies); datasets including complex types of analogies are typically manually curated and very small. We believe that this holds back progress in computational analogy. In this work, we design a data generation pipeline, ParallelPARC (Parallel Paragraph Creator) leveraging state-of-the-art Large Language Models (LLMs) to create complex, paragraph-based analogies, as well as distractors, both simple and challenging. We demonstrate our pipeline and create ProPara-Logy, a dataset of analogies between scientific processes. We publish a gold-set, validated by humans, and a silver-set, generated automatically. We test LLMs' and humans' analogy recognition in binary and multiple-choice settings, and found that humans outperform the best models (~13% gap) after a light supervision. We demonstrate that our silver-set is useful for training models. Lastly, we show challenging distractors confuse LLMs, but not humans. We hope our pipeline will encourage research in this emerging field.<|reference_end|>
@article{sultan2024parallelparc:,
    title={ParallelPARC: A Scalable Pipeline for Generating Natural-Language
  Analogies},
    author={Oren Sultan, Yonatan Bitton, Ron Yosef, Dafna Shahaf},
    journal={arXiv preprint arXiv:2403.01139},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01139},
    primaryClass={cs.CL cs.AI}
}