Summary of Enhancing Creativity in Large Language Models Through Associative Thinking Strategies, by Pronita Mehrotra et al.
Enhancing Creativity in Large Language Models through Associative Thinking Strategies
by Pronita Mehrotra, Aishni Parab, Sumit Gulwani
First submitted to arxiv on: 9 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 This paper investigates whether Large Language Models (LLMs) like vGPT-4 can be made more creative by employing associative thinking strategies, which have been shown to boost human creativity. The researchers designed three creativity tasks in Product Design, Storytelling, and Marketing domains to assess vGPT-4’s ability to generate original content when prompted to connect seemingly unrelated concepts. By challenging the model to form novel associations, they found that leveraging associative thinking techniques can significantly improve the originality of vGPT-4’s responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can be made more creative by using a special way of thinking called associative thinking. This is where you link things together that don’t seem related at first. It works for people and helps them come up with new ideas. But does it work for computers too? The researchers tested this idea on a big language model called vGPT-4, giving it tasks to design products, tell stories, and make marketing ideas. They wanted to see if the computer could come up with more original answers when asked to link unrelated things together. And the answer is yes – using associative thinking can really help computers be more creative. |
Keywords
» Artificial intelligence » Language model
