@inproceedings{lu-etal-2025-benchmarking,
title = "Benchmarking Language Model Creativity: A Case Study on Code Generation",
author = "Lu, Yining and
Wang, Dixuan and
Li, Tianjian and
Jiang, Dongwei and
Khudanpur, Sanjeev and
Jiang, Meng and
Khashabi, Daniel",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.141/",
doi = "10.18653/v1/2025.naacl-long.141",
pages = "2776--2794",
ISBN = "979-8-89176-189-6",
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: \textit{convergent} thinking (purposefulness to achieve a given goal) and \textit{divergent} thinking (adaptability to explore new environments or constraints) (CITATION). 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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<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: convergent thinking (purposefulness to achieve a given goal) and divergent thinking (adaptability to explore new environments or constraints) (CITATION). 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.</abstract>
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%0 Conference Proceedings
%T Benchmarking Language Model Creativity: A Case Study on Code Generation
%A Lu, Yining
%A Wang, Dixuan
%A Li, Tianjian
%A Jiang, Dongwei
%A Khudanpur, Sanjeev
%A Jiang, Meng
%A Khashabi, Daniel
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F lu-etal-2025-benchmarking
%X 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: convergent thinking (purposefulness to achieve a given goal) and divergent thinking (adaptability to explore new environments or constraints) (CITATION). 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.
%R 10.18653/v1/2025.naacl-long.141
%U https://aclanthology.org/2025.naacl-long.141/
%U https://doi.org/10.18653/v1/2025.naacl-long.141
%P 2776-2794
Markdown (Informal)
[Benchmarking Language Model Creativity: A Case Study on Code Generation](https://aclanthology.org/2025.naacl-long.141/) (Lu et al., NAACL 2025)
ACL
- Yining Lu, Dixuan Wang, Tianjian Li, Dongwei Jiang, Sanjeev Khudanpur, Meng Jiang, and Daniel Khashabi. 2025. Benchmarking Language Model Creativity: A Case Study on Code Generation. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 2776–2794, Albuquerque, New Mexico. Association for Computational Linguistics.