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

Abstract: LLMs have shown exceptional proficiency in natural language processing but often fall short of generating creative and original responses to open-ended questions. To enhance LLM creativity, our key insight is to emulate the human process of inducing collective creativity through engaging discussions with participants from diverse backgrounds and perspectives. To this end, we propose LLM Discussion, a three-phase discussion framework that facilitates vigorous and diverging idea exchanges and ensures convergence to creative answers. Moreover, we adopt a role-playing technique by assigning distinct roles to LLMs to combat the homogeneity of LLMs. We evaluate the efficacy of the proposed framework with the Alternative Uses Test, Similarities Test, Instances Test, and Scientific Creativity Test through both LLM evaluation and human study. The results show that our proposed framework outperforms single-LLM approaches and existing multi-LLM frameworks across various creativity metrics. The code is available at https://github.com/lawraa/LLM-Discussion.

• The paper demonstrates that a multi-phase discussion framework with role-play significantly enhances LLM creativity across established benchmarks.
• It introduces a three-phase process—initiation, discussion, and convergence—that encourages divergent thinking and mitigates homogeneous output.
• Evaluation using tests like the Alternative Uses and Scientific Creativity Tests revealed improved originality and elaboration, validating the framework's effectiveness.

Enhancing Creativity in LLMs via Collaborative Discussion and Role-Playing

The paper, "LLM Discussion: Enhancing the Creativity of LLMs via Discussion Framework and Role-Play," introduces an innovative approach to augment the creativity of LLMs by simulating multi-agent interaction akin to human brainstorming sessions. The authors propose a structured discussion framework that harnesses role-playing to diversify perspectives, thus fostering creative idea generation.

Framework Description

The proposed framework involves a three-phase discussion process designed to encourage dynamic idea exchanges among LLMs. The phases—initiation, discussion, and convergence—are tailored with specific prompts to maintain vigorous interaction and guide the LLMs toward producing creative outputs. By assigning distinct roles to each LLM, the approach aims to mitigate the limitations of homogeneity, which hinders creativity in standard collaborative LLM settings.

Evaluation and Results

The framework's effectiveness is assessed using four established creativity benchmarks: the Alternative Uses Test, Instances Test, Similarities Test, and Scientific Creativity Test. These benchmarks measure various aspects of creativity, including originality, elaboration, fluency, and flexibility. LLM-based evaluation and human studies reveal that the proposed method significantly enhances creative output compared to single-LLM and other multi-LLM frameworks. Notably, the LLM Discussion framework achieved higher scores in originality and elaboration metrics across all tests.

Key Insights and Observations

• Collaborative Dynamics: The structured interaction promotes divergent thinking by requiring LLMs to build upon each other's suggestions rather than converge prematurely, as observed in traditional debate-type frameworks.
• Role-Specific Contributions: By assigning roles like Environmentalist, Visionary Millionaire, and Futurist, the framework ensures that LLMs approach problems from diverse angles, enriching the discussion with unique insights.
• Conceptual Complexity: The enhanced elaboration scores suggest that role-play contributes to more detailed and intricately developed ideas, reflecting a broader range of thought and in-depth exploration.

Implications and Future Directions

This research opens avenues for applying the role-enhanced discussion framework to other facets of computational creativity and beyond. Practically, this could influence the design of creative AI applications, making them better suited for tasks requiring innovative problem-solving. Theoretically, it challenges the community to rethink how interactions between AI agents can be structured to maximize collective intelligence and creativity.

Future work could include integrating human participants into the discussion framework, exploring the synergy between human intuition and LLM's computational creativity, and further refining evaluation metrics to encompass nuanced aspects of creativity. Additionally, examining the framework's robustness across diverse LLM architectures and domains could provide insights into its generalized applicability.