Summary of Llm Discussion: Enhancing the Creativity Of Large Language Models Via Discussion Framework and Role-play, by Li-chun Lu et al.

LLM Discussion: Enhancing the Creativity of Large Language Models via Discussion Framework and Role-Play

by Li-Chun Lu, Shou-Jen Chen, Tsung-Min Pai, Chan-Hung Yu, Hung-yi Lee, Shao-Hua Sun

First submitted to arxiv on: 10 May 2024

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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 research proposes a framework called LLM Discussion to enhance the creativity of large language models (LLMs) in generating original responses. The approach involves emulating human discussions by assigning diverse roles to LLMs and facilitating idea exchanges through three phases: diverging, converging, and evaluating. The authors evaluate the efficacy of this framework using four creativity tests: Alternative Uses Test, Similarities Test, Instances Test, and Scientific Creativity Test. The results show that LLM Discussion outperforms single-LLM approaches and existing multi-LLM frameworks across various creativity metrics.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Large language models are great at answering questions, but they can struggle to come up with creative ideas on their own. To help them be more creative, the researchers came up with a new way of having LLMs talk to each other. They think that by giving different roles to the LLMs and letting them discuss things in three stages (first, everyone has an idea, then they work together, and finally, they come up with a solution), they can get more creative answers. To test this idea, they used four different tests to see how well it worked. And the results show that this new way of having LLMs talk is better than just having one LLM or using existing ways of combining multiple LLMs.