# Pre-Trained Large Language Models for Industrial Control

arXiv: https://arxiv.org/abs/2308.03028 · HTML: https://mlsanity.com/paper/2308.03028/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 56 of its 93 references that are in the map.
- First posted on arXiv 6 Aug 2023.

## Builds on (most cited first, 30 of 56)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)

## Built on by (most cited first, 16 of 16)

- [Autonomous Industrial Control using an Agentic Framework with Large Language Models](https://mlsanity.com/paper/2411.05904/) (2024)
- [Agentic LLMs in the Supply Chain: Towards Autonomous Multi-Agent Consensus-Seeking](https://mlsanity.com/paper/2411.10184/) (2024)
- [Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants](https://mlsanity.com/paper/2505.02076/) (2025)
- [Using Large Language Models for Parametric Shape Optimization](https://mlsanity.com/paper/2412.08072/) (2024)
- [Foundation Models for CPS-IoT: Opportunities and Challenges](https://mlsanity.com/paper/2501.16368/) (2025)
- [A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis](https://mlsanity.com/paper/2506.12263/) (2025)
- [A Status Quo Investigation of Large Language Models towards Cost-Effective CFD Automation with OpenFOAMGPT: ChatGPT vs. Qwen vs. Deepseek](https://mlsanity.com/paper/2504.02888/) (2025)
- [Training microrobots to swim by a large language model](https://mlsanity.com/paper/2402.00044/) (2024)
- [From Detection to Action: Using LLM Agents for Fault-Tolerant Control](https://mlsanity.com/paper/2606.28011/) (2026)
- [DCoPilot: Generative AI-Empowered Policy Adaptation for Dynamic Data Center Operations](https://mlsanity.com/paper/2602.02137/) (2026)
- [Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control](https://mlsanity.com/paper/2607.12856/) (2026)
- [FurniMAS: Language-Guided Furniture Decoration using Multi-Agent System](https://mlsanity.com/paper/2507.04770/) (2025)
- [A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agents](https://mlsanity.com/paper/2606.31635/) (2026)
- [Exact Action Values Are Not Enough: Rollout-Verified Reinforcement Fine-Tuning of a Reasoning Model for Multi-Zone VAV Control](https://mlsanity.com/paper/2607.27914/) (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)
- [Which Buildings Are Artificial Intelligence-Ready? A Measurement-Based Assessment Framework for AI Question Answering and Actuation](https://mlsanity.com/paper/2610.09119/) (2026)

## Abstract (arXiv)

For industrial control, developing high-performance controllers with few samples and low technical debt is appealing. Foundation models, possessing rich prior knowledge obtained from pre-training with Internet-scale corpus, have the potential to be a good controller with proper prompts. In this paper, we take HVAC (Heating, Ventilation, and Air Conditioning) building control as an example to examine the ability of GPT-4 (one of the first-tier foundation models) as the controller. To control HVAC, we wrap the task as a language game by providing text including a short description for the task, several selected demonstrations, and the current observation to GPT-4 on each step and execute the actions responded by GPT-4. We conduct series of experiments to answer the following questions: 1)~How well can GPT-4 control HVAC? 2)~How well can GPT-4 generalize to different scenarios for HVAC control? 3) How different parts of the text context affect the performance? In general, we found GPT-4 achieves the performance comparable to RL methods with few samples and low technical debt, indicating the potential of directly applying foundation models to industrial control tasks.