Spark: A System for Scientifically Creative Idea Generation
This work addresses the challenge of automating creative idea generation in science for computational creativity researchers, though it appears incremental as it builds on existing LLM and retrieval methods.
The authors tackled the problem of generating scientifically creative ideas by introducing Spark, a system that combines retrieval-augmented LLMs with a reviewer model trained on 600K scientific reviews, resulting in a released dataset to inspire further research in computational creativity.
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.