LLM4PLC: Harnessing Large Language Models for Verifiable Programming of
  PLCs in Industrial Control Systems
Mohamad Fakih Rahul Dharmaji Yasamin Moghaddas Gustavo Quiros Araya Oluwatosin Ogundare Mohammad Abdullah Al Faruque
Although Large Language Models (LLMs) have established pre-dominance in
automated code generation, they are not devoid of shortcomings. The pertinent
issues primarily relate to the absence of execution guarantees for generated
code, a lack of explainability, and suboptimal support for essential but niche
programming languages. State-of-the-art LLMs such as GPT-4 and LLaMa2 fail to
produce valid programs for Industrial Control Systems (ICS) operated by
Programmable Logic Controllers (PLCs). We propose LLM4PLC, a user-guided
iterative pipeline leveraging user feedback and external verification tools
including grammar checkers, compilers and SMV verifiers to guide the LLM's
generation. We further enhance the generation potential of LLM by employing
Prompt Engineering and model fine-tuning through the creation and usage of
LoRAs. We validate this system using a FischerTechnik Manufacturing TestBed
(MFTB), illustrating how LLMs can evolve from generating structurally flawed
code to producing verifiably correct programs for industrial applications. We
run a complete test suite on GPT-3.5, GPT-4, Code Llama-7B, a fine-tuned Code
Llama-7B model, Code Llama-34B, and a fine-tuned Code Llama-34B model. The
proposed pipeline improved the generation success rate from 47% to 72%, and the
Survey-of-Experts code quality from 2.25/10 to 7.75/10. To promote open
research, we share the complete experimental setup, the LLM Fine-Tuning
Weights, and the video demonstrations of the different programs on our
dedicated webpage.