Geotechnical Parrot Tales (GPT): Harnessing Large Language Models in geotechnical engineering
This work tackles the challenge of unreliable AI outputs for geotechnical engineers, but it appears incremental as it focuses on applying existing methods to a specific domain.
The paper addresses the problem of hallucinations in large language models (LLMs) like GPT when applied to geotechnical engineering, proposing prompt engineering and a unified natural language interface to mitigate risks and enhance workflow integration.
The widespread adoption of large language models (LLMs), such as OpenAI's ChatGPT, could revolutionize various industries, including geotechnical engineering. However, GPT models can sometimes generate plausible-sounding but false outputs, leading to hallucinations. In this article, we discuss the importance of prompt engineering in mitigating these risks and harnessing the full potential of GPT for geotechnical applications. We explore the challenges and pitfalls associated with LLMs and highlight the role of context in ensuring accurate and valuable responses. Furthermore, we examine the development of context-specific search engines and the potential of LLMs to become a natural interface for complex tasks, such as data analysis and design. We also develop a unified interface using natural language to handle complex geotechnical engineering tasks and data analysis. By integrating GPT into geotechnical engineering workflows, professionals can streamline their work and develop sustainable and resilient infrastructure systems for the future.