Summary of Liveideabench: Evaluating Llms’ Scientific Creativity and Idea Generation with Minimal Context, by Kai Ruan et al.
LiveIdeaBench: Evaluating LLMs’ Scientific Creativity and Idea Generation with Minimal Context
by Kai Ruan, Xuan Wang, Jixiang Hong, Peng Wang, Yang Liu, Hao Sun
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Large Language Model (LLM) benchmark is proposed to evaluate scientific creativity and divergent thinking capabilities, which are often overlooked in existing evaluation frameworks. The LiveIdeaBench framework assesses generated ideas from single-keyword prompts across four dimensions: originality, feasibility, fluency, and flexibility. Extensive experimentation with 20 leading models across 18 scientific domains reveals distinct patterns of scientific creative ability separate from general intelligence metrics. Notably, QwQ-32B-preview achieves comparable creative performance to top-tier models like o1-preview, despite significant gaps in their general intelligence scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to test how well computers can come up with creative ideas is being developed. This approach focuses on giving computers single-word prompts and seeing what they come up with. The goal is to create a better understanding of how well computers can think creatively, rather than just solving problems. The results show that some computers are much better at coming up with creative ideas than others, even if they’re not as good at solving general problems. |
Keywords
» Artificial intelligence » Large language model
