Literature-Grounded Novelty Assessment of Scientific Ideas

• URL: http://arxiv.org/abs/2506.22026v1
• Date: Fri, 27 Jun 2025 08:47:28 GMT
• Title: Literature-Grounded Novelty Assessment of Scientific Ideas
• Authors: Simra Shahid, Marissa Radensky, Raymond Fok, Pao Siangliulue, Daniel S. Weld, Tom Hope,
• Abstract summary: We propose the Idea Novelty Checker, an LLM-based retrieval-augmented generation framework.<n>Our experiments demonstrate that our novelty checker achieves approximately 13% higher agreement than existing approaches.
• Score: 23.481266336046833
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated scientific idea generation systems have made remarkable progress, yet the automatic evaluation of idea novelty remains a critical and underexplored challenge. Manual evaluation of novelty through literature review is labor-intensive, prone to error due to subjectivity, and impractical at scale. To address these issues, we propose the Idea Novelty Checker, an LLM-based retrieval-augmented generation (RAG) framework that leverages a two-stage retrieve-then-rerank approach. The Idea Novelty Checker first collects a broad set of relevant papers using keyword and snippet-based retrieval, then refines this collection through embedding-based filtering followed by facet-based LLM re-ranking. It incorporates expert-labeled examples to guide the system in comparing papers for novelty evaluation and in generating literature-grounded reasoning. Our extensive experiments demonstrate that our novelty checker achieves approximately 13% higher agreement than existing approaches. Ablation studies further showcases the importance of the facet-based re-ranker in identifying the most relevant literature for novelty evaluation.

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