AI Idea Bench 2025: AI Research Idea Generation Benchmark
This addresses the problem of limited evaluation in AI idea generation for researchers, though it is incremental as it builds on existing benchmarking efforts.
The paper tackles the lack of comprehensive benchmarks for evaluating AI research idea generation by LLMs, introducing AI Idea Bench 2025, a framework with a dataset of 3,495 AI papers and a methodology that assesses idea quality based on alignment with ground-truth content and general references.
Large-scale Language Models (LLMs) have revolutionized human-AI interaction and achieved significant success in the generation of novel ideas. However, current assessments of idea generation overlook crucial factors such as knowledge leakage in LLMs, the absence of open-ended benchmarks with grounded truth, and the limited scope of feasibility analysis constrained by prompt design. These limitations hinder the potential of uncovering groundbreaking research ideas. In this paper, we present AI Idea Bench 2025, a framework designed to quantitatively evaluate and compare the ideas generated by LLMs within the domain of AI research from diverse perspectives. The framework comprises a comprehensive dataset of 3,495 AI papers and their associated inspired works, along with a robust evaluation methodology. This evaluation system gauges idea quality in two dimensions: alignment with the ground-truth content of the original papers and judgment based on general reference material. AI Idea Bench 2025's benchmarking system stands to be an invaluable resource for assessing and comparing idea-generation techniques, thereby facilitating the automation of scientific discovery.