Summary of Parallelparc: a Scalable Pipeline For Generating Natural-language Analogies, by Oren Sultan et al.
ParallelPARC: A Scalable Pipeline for Generating Natural-Language Analogies
by Oren Sultan, Yonatan Bitton, Ron Yosef, Dafna Shahaf
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel data generation pipeline, ParallelPARC, utilizes Large Language Models to create complex paragraph-based analogies and distractors, enabling the development of a larger dataset for training AI systems to recognize analogies. The ProPara-Logy dataset includes scientific process analogies, with both gold-standard human-validated examples and automatically generated silver-set examples. While humans outperform AI models in analogy recognition tasks, the silver-set is found to be useful for training models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers are trying to make computers better at understanding analogies, which are important for adapting to new situations. Right now, most datasets only include simple analogies and are very small because they’re made by humans. We created a way to generate more complex analogies using powerful language models, which can help us develop AI systems that are better at making connections between ideas. |
