Spark: A System for Scientifically Creative Idea Generation
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909619447660544 |
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| author | Sanyal, Aishik Schapiro, Samuel Shashidhar, Sumuk Moon, Royce Varshney, Lav R. Hakkani-Tur, Dilek |
| author_facet | Sanyal, Aishik Schapiro, Samuel Shashidhar, Sumuk Moon, Royce Varshney, Lav R. Hakkani-Tur, Dilek |
| contents | Recently, large language models (LLMs) have shown promising abilities to generate novel research ideas in science, a direction which coincides with many foundational principles in computational creativity (CC). In light of these developments, we present an idea generation system named Spark that couples retrieval-augmented idea generation using LLMs with a reviewer model named Judge trained on 600K scientific reviews from OpenReview. Our work is both a system demonstration and intended to inspire other CC researchers to explore grounding the generation and evaluation of scientific ideas within foundational CC principles. To this end, we release the annotated dataset used to train Judge, inviting other researchers to explore the use of LLMs for idea generation and creative evaluations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20090 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Spark: A System for Scientifically Creative Idea Generation Sanyal, Aishik Schapiro, Samuel Shashidhar, Sumuk Moon, Royce Varshney, Lav R. Hakkani-Tur, Dilek Artificial Intelligence Information Retrieval Machine Learning Recently, large language models (LLMs) have shown promising abilities to generate novel research ideas in science, a direction which coincides with many foundational principles in computational creativity (CC). In light of these developments, we present an idea generation system named Spark that couples retrieval-augmented idea generation using LLMs with a reviewer model named Judge trained on 600K scientific reviews from OpenReview. Our work is both a system demonstration and intended to inspire other CC researchers to explore grounding the generation and evaluation of scientific ideas within foundational CC principles. To this end, we release the annotated dataset used to train Judge, inviting other researchers to explore the use of LLMs for idea generation and creative evaluations. |
| title | Spark: A System for Scientifically Creative Idea Generation |
| topic | Artificial Intelligence Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2504.20090 |