Benchmarking Language Model Creativity: A Case Study on Code Generation

• URL: http://arxiv.org/abs/2407.09007v2
• Date: Sat, 08 Feb 2025 19:32:51 GMT
• Title: Benchmarking Language Model Creativity: A Case Study on Code Generation
• Authors: Yining Lu, Dixuan Wang, Tianjian Li, Dongwei Jiang, Sanjeev Khudanpur, Meng Jiang, Daniel Khashabi,
• Abstract summary: In this work, we introduce a framework for quantifying LLM creativity.<n>We define NEOGAUGE, a metric that quantifies both convergent and divergent thinking in the generated creative responses.<n>We test the proposed framework on Codeforces problems, which serve as both a natural dataset for coding tasks and a collection of prior human solutions.
• Score: 39.546827184857754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As LLMs become increasingly prevalent, it is interesting to consider how ``creative'' these models can be. From cognitive science, creativity consists of at least two key characteristics: \emph{convergent} thinking (purposefulness to achieve a given goal) and \emph{divergent} thinking (adaptability to explore new environments or constraints) \citep{runco2003critical}. In this work, we introduce a framework for quantifying LLM creativity that incorporates the two design ingredients: (1) We introduce DENIAL PROMPTING which pushes LLMs to develop more creative solutions to a given problem by incrementally imposing new constraints on the previous solution, compelling LLMs to adopt new strategies. (2) We define NEOGAUGE, a metric that quantifies both convergent and divergent thinking in the generated creative responses by LLMs. We test the proposed framework on Codeforces problems, which serve as both a natural dataset for coding tasks and a collection of prior human solutions. We quantify NEOGAUGE for various proprietary and open-source models and find that even the most creative model, GPT-4, still falls short of demonstrating human-like creativity. We also experiment with advanced reasoning strategies (MCTS, self-correction, etc.) and observe no significant improvement in creativity. As a by-product of our analysis, we release NEOCODER dataset for reproducing our results on future models.

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