TRIZ-GPT: An LLM-augmented method for problem-solving
<|reference_start|>TRIZ-GPT: An LLM-augmented method for problem-solving: TRIZ, the Theory of Inventive Problem Solving, is derived from a comprehensive analysis of patents across various domains, offering a framework and practical tools for problem-solving. Despite its potential to foster innovative solutions, the complexity and abstractness of TRIZ methodology often make its acquisition and application challenging. This often requires users to have a deep understanding of the theory, as well as substantial practical experience and knowledge across various disciplines. The advent of Large Language Models (LLMs) presents an opportunity to address these challenges by leveraging their extensive knowledge bases and reasoning capabilities for innovative solution generation within TRIZ-based problem-solving process. This study explores and evaluates the application of LLMs within the TRIZ-based problem-solving process. The construction of TRIZ case collections establishes a solid empirical foundation for our experiments and offers valuable resources to the TRIZ community. A specifically designed workflow, utilizing step-by-step reasoning and evaluation-validated prompt strategies, effectively transforms concrete problems into TRIZ problems and finally generates inventive solutions. Finally, we present a case study in mechanical engineering field that highlights the practical application of this LLM-augmented method. It showcases GPT-4's ability to generate solutions that closely resonate with original solutions and suggests more implementation mechanisms.<|reference_end|>
@article{chen2024triz-gpt:,
    title={TRIZ-GPT: An LLM-augmented method for problem-solving},
    author={Liuqing Chen, Yaxuan Song, Shixian Ding, Lingyun Sun, Peter Childs,
  and Haoyu Zuo},
    journal={arXiv preprint arXiv:2408.05897},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05897},
    primaryClass={cs.HC}
}