mlsanityData to 13 Oct 2026

Geotechnical Parrot Tales (GPT): Harnessing Large Language Models in geotechnical engineering

arXiv 2304.02138 ↗First posted 4 Apr 2023cs.CL physics.geo-phcited by 4

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 6 of its 9 references that are in the map. First posted on arXiv 4 Apr 2023.

Builds on

6 of 9 references.

References · 6

  1. 2017Attention Is All You Needcited by 60,106
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,072
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,376
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,502
  5. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,586
  6. 2020How Context Affects Language Models' Factual Predictionscited by 181

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 0; 2025: 1.

4 papers.