# LLM4PLC: Harnessing Large Language Models for Verifiable Programming of PLCs in Industrial Control Systems

arXiv: https://arxiv.org/abs/2401.05443 · HTML: https://mlsanity.com/paper/2401.05443/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 62 references that are in the map.
- First posted on arXiv 8 Jan 2024.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://mlsanity.com/paper/2205.05638/) (2022)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning](https://mlsanity.com/paper/2207.01780/) (2022)
- [A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models](https://mlsanity.com/paper/2303.10420/) (2023)
- [VeriGen: A Large Language Model for Verilog Code Generation](https://mlsanity.com/paper/2308.00708/) (2023)
- [DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task](https://mlsanity.com/paper/2304.01097/) (2023)
- [Examining Zero-Shot Vulnerability Repair with Large Language Models](https://mlsanity.com/paper/2112.02125/) (2021)
- [Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models](https://mlsanity.com/paper/2208.07852/) (2022)
- [AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks](https://mlsanity.com/paper/2306.08107/) (2023)
- [Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases](https://mlsanity.com/paper/2308.09313/) (2023)
- [Parameter-Efficient Tuning Makes a Good Classification Head](https://mlsanity.com/paper/2210.16771/) (2022)
- [FRANC: A Lightweight Framework for High-Quality Code Generation](https://mlsanity.com/paper/2307.08220/) (2023)
- [The potential of LLMs for coding with low-resource and domain-specific programming languages](https://mlsanity.com/paper/2307.13018/) (2023)
- [SYNFIX: Automatically Fixing Syntax Errors using Compiler Diagnostics](https://mlsanity.com/paper/2104.14671/) (2021)
- [CFG2VEC: Hierarchical Graph Neural Network for Cross-Architectural Software Reverse Engineering](https://mlsanity.com/paper/2301.02723/) (2023)
- [On Formal Reasoning on the Semantics of PLC using Coq](https://mlsanity.com/paper/1301.3047/) (2013)

## Built on by (most cited first, 21 of 21)

- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages](https://mlsanity.com/paper/2410.03981/) (2024)
- [Agents4PLC: Automating Closed-loop PLC Code Generation and Verification in Industrial Control Systems using LLM-based Agents](https://mlsanity.com/paper/2410.14209/) (2024)
- [Large Language Model-Enabled Multi-Agent Manufacturing Systems](https://mlsanity.com/paper/2406.01893/) (2024)
- [Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants](https://mlsanity.com/paper/2505.02076/) (2025)
- [Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation](https://mlsanity.com/paper/2507.07115/) (2025)
- [MCCoder: Streamlining Motion Control with LLM-Assisted Code Generation and Rigorous Verification](https://mlsanity.com/paper/2410.15154/) (2024)
- [3D Software Synthesis Driven by Constraint-Expressive Intermediate Representation](https://mlsanity.com/paper/2507.18625/) (2025)
- [AutoPLC: Generating Vendor-Aware Structured Text for Programmable Logic Controllers](https://mlsanity.com/paper/2412.02410/) (2024)
- [Domain Specific Benchmarks for Evaluating Multimodal Large Language Models](https://mlsanity.com/paper/2506.12958/) (2025)
- [INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection](https://mlsanity.com/paper/2411.10918/) (2024)
- [Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfaces](https://mlsanity.com/paper/2505.03295/) (2025)
- [DCoPilot: Generative AI-Empowered Policy Adaptation for Dynamic Data Center Operations](https://mlsanity.com/paper/2602.02137/) (2026)
- [Utilizing LLMs for Industrial Process Automation: A Case Study on Modifying RAPID Programs](https://mlsanity.com/paper/2511.11125/) (2025)
- [Downsides of Smartness Across Edge-Cloud Continuum in Modern Industry](https://mlsanity.com/paper/2603.29289/) (2026)
- [ROSUM-MCTS: Monte Carlo Tree Search-Inspired HDL Code Summarization with Structural Rewards](https://mlsanity.com/paper/2606.07925/) (2026)
- [RAG-Based Auto-Configuration for Industrial Fieldbus Devices](https://mlsanity.com/paper/2608.08618/) (2026)
- [Large Language Models in the Loop: A Stability- and Network-Aware Survey in Networked Control, Cyber-Physical, and Multi-Agent Systems](https://mlsanity.com/paper/2609.16599/) (2026)
- [Requirement-Bound Verified Commissioning: A Frozen Four-Billion-Parameter Local Model as a Candidate Generator under an External Acceptance Layer with Verification and Release Authority](https://mlsanity.com/paper/2609.30219/) (2026)
- [From Dead Code and Static Requirements to Working Engines: Software Revival with Coding Agents](https://mlsanity.com/paper/2609.36161/) (2026)
- [PLCWorld: Benchmarking LLM-Generated PLC Programs in Closed-Loop Plant Simulation](https://mlsanity.com/paper/2610.02982/) (2026)

## Abstract (arXiv)

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.