AI Idea Bench 2025: AI Research Idea Generation Benchmark (2504.14191v3)

Abstract: Large-scale 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.

• The paper introduces a novel benchmark framework utilizing a dataset of 3,495 AI papers to assess the quality, innovativeness, and feasibility of LLM-generated research ideas.
• The evaluation methodology rigorously measures idea alignment with original paper content and referenced materials, addressing challenges like knowledge leakage and prompt bias.
• Extensive experiments validate the framework’s effectiveness in highlighting model strengths and weaknesses, paving the way for enhanced AI-driven scientific discovery.

AI Idea Bench 2025: A Framework for AI Research Idea Evaluation

The paper "AI Idea Bench 2025: AI Research Idea Generation Benchmark" introduces a framework aimed at evaluating and comparing the efficacy of Large-Scale LLMs in generating AI research ideas. This framework is crucial because current assessments overlook critical factors such as knowledge leakage, lack of open-ended benchmarks, and the limited scope of feasibility analysis constrained by prompt design, all of which impede the discovery of innovative research ideas.

The researchers present AI Idea Bench 2025, consisting of a comprehensive dataset of 3,495 AI papers accompanied by inspired works and a robust evaluation methodology. This dual-faceted evaluation assesses idea quality based on alignment with ground-truth content of original papers and general reference materials. The framework is presented as a valuable asset for benchmarking idea-generation techniques in an era where automating scientific discovery is of significant interest.

Key Methodological Contributions

1. Dataset Construction: The creators of AI Idea Bench 2025 assembled a dataset composed of 3,495 influential AI-related papers from top conferences published after the LLMs' knowledge cutoff date. This approach mitigates the risk of data leakage and enhances the robustness of the evaluation methodology.
2. Evaluation Framework: The framework evaluates ideas through two primary components - alignment with ground-truth paper content and referenced evaluations compared to ideas generated by different baselines. The framework holistically assesses idea innovativeness, feasibility, and overall quality using objective criteria.
3. Comprehensive Experiments: The researchers conducted extensive experiments to illustrate the dataset and framework's efficacy in fostering innovative research ideas within the AI domain.

Implications and Future Directions

The implications of AI Idea Bench 2025 are both practical and theoretical. Practically, it serves as a tool for researchers and developers to critically assess the idea generation capabilities of LLMs, identifying strengths and weaknesses across different models. Theoretically, the framework might influence further development of more sophisticated LLMs that better account for context and creativity in scientific discovery.

The potential future advancements in AI might include developing expanded benchmarks incorporating diverse scientific domains, encouraging interdisciplinary research, and ensuring that AI-driven idea generation can be seamlessly integrated into conventional scientific methods.

AI Idea Bench 2025 provides a significant step toward understanding and improving AI's role in creative scientific endeavors. Evaluating LLMs in structured and quantitative ways can lead to optimized AI tools, enhancing their utility in generating relevant and impactful research ideas within AI and beyond.