see the post for paper and gitrepo!
#100x prompts for IEC 61131 ST Code generation
hollow panther
here, have it =)
Post: https://arxiv.org/abs/2305.15809
Direct link to PDF: https://arxiv.org/pdf/2305.15809.pdf
@shut fjord are you involved with this paper?
white vault
Paper title at arXiv --> "ChatGPT for PLC/DCS Control Logic Generation"!
Could you give us a summary of the key takeaway points for day to day users and some use cases?
keen hemlock
I haven't fact-checked this summary, but an AI summary of the paper brought this result:
Summary:
The paper looked at whether AI models like ChatGPT can generate useful computer code for industrial control systems. These control systems are used in factories, power plants etc to automate processes.
The researchers gave ChatGPT prompts across 10 categories to see if it could generate valid code in a standard language called IEC 61131-3 Structured Text.
In many cases, ChatGPT produced syntactically correct, efficient code for tasks like math functions, process control, etc.
ChatGPT also showed reasoning skills on real-world concepts and domain knowledge on industrial systems.
The quality of its answers depended heavily on how specific and detailed the prompts were. More context through multiple prompts improved results.
The code still needs oversight from human experts before deployment. But the authors feel these models could significantly boost programmer productivity.
The paper offers the first comprehensive test collection focused specifically on industrial control logic generation using AI.
Suggestions for general users:
Use ChatGPT for drafts and prototypes but review code thoroughly before use.
Provide lots of context e.g. explain the goal, constraints, edge cases etc.
Ask narrow, specific questions rather than broad ones.
Refine prompts iteratively if the initial answer is inadequate.
Validate answers using tests, simulations or expert reviews.
Use ChatGPT's explanations to build your own knowledge too.
Be wary of over-reliance on AI-generated text or code without human checks.
In summary, the paper demonstrates promising capabilities but also limitations of AI models for generating industrial control logic today.
Discussion Points:
How might LLMs change control engineering education and training?
How to determine if custom training is needed vs leveraging general capabilities?
What new prompting skills will engineers need to use LLMs effectively?