Utilizing LLMs for Industrial Process Automation: A Case Study on Modifying RAPID Programs

• URL: http://arxiv.org/abs/2511.11125v1
• Date: Fri, 14 Nov 2025 09:56:55 GMT
• Title: Utilizing LLMs for Industrial Process Automation: A Case Study on Modifying RAPID Programs
• Authors: Salim Fares, Steffen Herbold,
• Abstract summary: This paper studies the utility of Large Language Models for software within the industrial process automation domain.<n>We show that few-shot prompting approaches are sufficient to solve simple problems in a language that is otherwise not well-supported by an LLM.
• Score: 3.344552881770396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How to best use Large Language Models (LLMs) for software engineering is covered in many publications in recent years. However, most of this work focuses on widely-used general purpose programming languages. The utility of LLMs for software within the industrial process automation domain, with highly-specialized languages that are typically only used in proprietary contexts, is still underexplored. Within this paper, we study enterprises can achieve on their own without investing large amounts of effort into the training of models specific to the domain-specific languages that are used. We show that few-shot prompting approaches are sufficient to solve simple problems in a language that is otherwise not well-supported by an LLM and that is possible on-premise, thereby ensuring the protection of sensitive company data.

Related papers

• Utilizing LLMs for Industrial Process Automation [0.0]
  Large Language Models (LLMs) can be used to solve real-life programming tasks.<n>This research aims to utilize and integrate LLMs in the industrial development process.
  arXiv  Detail & Related papers  (2026-02-26T18:38:00Z)
• A Study of LLMs' Preferences for Libraries and Programming Languages [19.688657440697632]
  This study is the first empirical study of Large Language Models' preferences for libraries and programming languages when generating code.<n>Our results reveal that LLMs exhibit a strong tendency to overuse widely adopted libraries such as NumPy.<n>For high-performance project initialisation tasks where Python is not the optimal language, it remains the dominant choice in 58% of cases, and Rust is not used a single time.
  arXiv  Detail & Related papers  (2025-03-21T14:29:35Z)
• Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey [45.918134803778834]
  Large Language Models (LLMs) have demonstrated remarkable success in various tasks such as natural language understanding, text summarization, and machine translation.<n>Their general-purpose nature often limits their effectiveness in domain-specific applications that require specialized knowledge, such as healthcare, chemistry, or legal analysis.<n>To address this, researchers have explored diverse methods to enhance LLMs by integrating domain-specific knowledge.
  arXiv  Detail & Related papers  (2025-02-15T07:43:43Z)
• Enhancing Code Generation for Low-Resource Languages: No Silver Bullet [55.39571645315926]
  Large Language Models (LLMs) rely on large and diverse datasets to learn syntax, semantics, and usage patterns of programming languages.<n>For low-resource languages, the limited availability of such data hampers the models' ability to generalize effectively.<n>We present an empirical study investigating the effectiveness of several approaches for boosting LLMs' performance on low-resource languages.
  arXiv  Detail & Related papers  (2025-01-31T12:23:28Z)
• LLMic: Romanian Foundation Language Model [76.09455151754062]
  We present LLMic, a foundation language model designed specifically for the Romanian Language.<n>We show that fine-tuning LLMic for language translation after the initial pretraining phase outperforms existing solutions in English-to-Romanian translation tasks.
  arXiv  Detail & Related papers  (2025-01-13T22:14:45Z)
• Studying and Benchmarking Large Language Models For Log Level Suggestion [49.176736212364496]
  Large Language Models (LLMs) have become a focal point of research across various domains. This paper investigates the impact of characteristics and learning paradigms on the performance of 12 open-source LLMs in log level suggestion.
  arXiv  Detail & Related papers  (2024-10-11T03:52:17Z)
• Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian [75.94354349994576]
  This paper explores the feasibility of employing smaller, domain-specific encoder LMs alongside prompting techniques to enhance performance in specialized contexts. Our study concentrates on the Italian bureaucratic and legal language, experimenting with both general-purpose and further pre-trained encoder-only models. The results indicate that while further pre-trained models may show diminished robustness in general knowledge, they exhibit superior adaptability for domain-specific tasks, even in a zero-shot setting.
  arXiv  Detail & Related papers  (2024-07-30T08:50:16Z)
• BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models [56.89958793648104]
  Large Language Models (LLMs) are versatile and capable of addressing a diverse range of tasks. Previous approaches either conduct continuous pre-training with domain-specific data or employ retrieval augmentation to support general LLMs. We present a novel framework named BLADE, which enhances Black-box LArge language models with small Domain-spEcific models.
  arXiv  Detail & Related papers  (2024-03-27T08:57:21Z)
• The potential of LLMs for coding with low-resource and domain-specific programming languages [0.0]
  This study focuses on the econometric scripting language named hansl of the open-source software gretl. Our findings suggest that LLMs can be a useful tool for writing, understanding, improving, and documenting gretl code.
  arXiv  Detail & Related papers  (2023-07-24T17:17:13Z)
• How Effective are Large Language Models in Generating Software Specifications? [14.170320751508502]
  Large Language Models (LLMs) have been successfully applied to numerous Software Engineering (SE) tasks.<n>We conduct the first empirical study to evaluate the capabilities of LLMs for generating software specifications from software comments or documentation.
  arXiv  Detail & Related papers  (2023-06-06T00:28:39Z)
• Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey [100.24095818099522]
  Large language models (LLMs) have significantly advanced the field of natural language processing (NLP) They provide a highly useful, task-agnostic foundation for a wide range of applications. However, directly applying LLMs to solve sophisticated problems in specific domains meets many hurdles.
  arXiv  Detail & Related papers  (2023-05-30T03:00:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.