Papers › ParallelPARC: A Scalable Pipeline for Generating Natural-Language Analogies

ParallelPARC: A Scalable Pipeline for Generating Natural-Language Analogies

2 Mar 2024arXiv:2403.01139archive 2025-07-28

Oren Sultan, Yonatan Bitton, Ron Yosef, Dafna Shahaf

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.

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call_gpt3 orensul/parallelparc/pipeline/analogy_candidates_generation/generate_analogy_candidates.py official repository ran · our draft was wrong MIT (permissive) · 0a1b0984fcb66112 · report
create_propara_files orensul/parallelparc/pipeline/analogy_candidates_generation/generate_analogy_candidates.py official repository ran MIT (permissive) · efd493d4628930a7 · report
extract_relations orensul/parallelparc/pipeline/analogy_candidates_generation/generate_analogy_candidates.py official repository ran fingerprinted MIT (permissive) · d687e73c1476d3d3 · report
get_paragraph_titles orensul/parallelparc/pipeline/analogy_candidates_generation/generate_analogy_candidates.py official repository ran · our draft was wrong MIT (permissive) · 117c39f1fcdce9e9 · report
read_propara_paragraphs orensul/parallelparc/pipeline/analogy_candidates_generation/generate_analogy_candidates.py official repository ran · our draft was wrong MIT (permissive) · e60e10a04916f0ce · report
gpt3_generate_target_propara_topics_analogies orensul/parallelparc/pipeline/analogy_candidates_generation/generate_analogy_candidates.py official repository unverified MIT (permissive) · 03dddbc5bdef095d · report

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