Summary of Divergent Creativity in Humans and Large Language Models, by Antoine Bellemare-pepin (1 and 2) et al.
Divergent Creativity in Humans and Large Language Models
by Antoine Bellemare-Pepin, François Lespinasse, Philipp Thölke, Yann Harel, Kory Mathewson, Jay A. Olson, Yoshua Bengio, Karim Jerbi
First submitted to arxiv on: 13 May 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 The recent advancements in Large Language Models (LLMs) have raised questions about their creativity levels. This paper aims to systematically evaluate LLM creativity by developing a framework for analyzing divergent thinking in both state-of-the-art LLMs and a dataset of 100,000 humans. The results suggest that LLMs can surpass human capabilities in specific creative tasks like divergent association and creative writing. A quantitative benchmarking framework is proposed, which opens up new paths for the development of more creative LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are getting really good at doing things on their own, kind of like humans! But what does it mean when they’re “creative”? This paper wants to figure that out by comparing how well LLMs do certain tasks with how humans do them. They looked at a lot of data and found that LLMs can actually be better than people at some things, like making up new ideas or writing stories. This might help make even more creative computers in the future! |
